Kaizhu Huang

Journal Article Pattern Recognition · February 1, 2025 3D point cloud analysis has recently garnered significant attention due to its capacity to provide more comprehensive information compared to 2D images. To confront the inherent irregular and unstructured properties of point clouds, recent research efforts ... Full text Cite

Biomedical Information Retrieval with Positive-Unlabeled Learning and Knowledge Graphs

Journal Article ACM Transactions on Intelligent Systems and Technology · November 4, 2024 The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedica ... Full text Cite

A generalizable framework for low-rank tensor completion with numerical priors

Journal Article Pattern Recognition · November 1, 2024 Low-Rank Tensor Completion, a method which exploits the inherent structure of tensors, has been studied extensively as an effective approach to tensor completion. Whilst such methods attained great success, none have systematically considered exploiting th ... Full text Cite

Document Registration: Towards Automated Labeling of Pixel-Level Alignment Between Warped-Flat Documents

Conference Proceedings of the 32nd ACM International Conference on Multimedia · October 28, 2024 Full text Cite

Open-Pose 3D zero-shot learning: Benchmark and challenges.

Journal Article Neural networks : the official journal of the International Neural Network Society · October 2024 With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, methods transferring language or language-image pre-training models like Contrastive Language-Image Pre-training (CLIP) to ... Full text Cite

EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction With Exogenous Variables.

Journal Article IEEE transactions on cybernetics · September 2024 While exogenous variables have a major impact on performance improvement in time series analysis, interseries correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time ser ... Full text Cite

SaliencyCut: Augmenting plausible anomalies for anomaly detection

Journal Article Pattern Recognition · September 1, 2024 Anomaly detection under the open-set scenario is a challenging task that requires learning discriminative features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to creat ... Full text Cite

Sim-to-Real Global Maximum Power Point Tracking With Domain Randomization and Adaptation for Photovoltaic Systems

Journal Article IEEE Journal of Emerging and Selected Topics in Industrial Electronics · July 2024 Full text Cite

Absorb and Repel: Pseudo-Label Refinement for Intra-Camera Supervised Person Re-Identification

Journal Article IEEE Transactions on Artificial Intelligence · June 1, 2024 Person re-identification (ReID) aims to identify pedestrian images with the same identity across non-overlapping camera views. Intra-camera supervised person re-identification (ICS-ReID) is a new paradigm that trains a model using only intra-camera labels, ... Full text Cite

Continuous Image Outpainting with Neural ODE

Journal Article ACM Transactions on Multimedia Computing, Communications and Applications · April 25, 2024 Generalised image outpainting is an important and active research topic in computer vision, which aims to extend appealing content all-side around a given image. Existing state-of-the-art outpainting methods often rely on discrete extrapolation to extend t ... Full text Cite

Perturbation diversity certificates robust generalization.

Journal Article Neural networks : the official journal of the International Neural Network Society · April 2024 Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers from over-fitting on training adversarial data and thus may not guarantee the robust generalization. This ... Full text Cite

Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning.

Journal Article Neural computation · April 2024 Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and u ... Full text Cite

Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial Training

Journal Article ACM Transactions on Information Systems · March 22, 2024 In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for i ... Full text Cite

Learning Disentangled Graph Convolutional Networks Locally and Globally.

Journal Article IEEE transactions on neural networks and learning systems · March 2024 Graph convolutional networks (GCNs) emerge as the most successful learning models for graph-structured data. Despite their success, existing GCNs usually ignore the entangled latent factors typically arising in real-world graphs, which results in nonexplai ... Full text Cite

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · February 1, 2024 Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In t ... Full text Cite

Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment

Journal Article ACM Transactions on Multimedia Computing, Communications and Applications · January 11, 2024 Scene text recognition (STR), one typical sequence-to-sequence problem, has drawn much attention recently in multimedia applications. To guarantee good performance, it is essential for STR to obtain aligned character-wise features from the whole-image feat ... Full text Cite

EPtask: Deep Reinforcement Learning Based Energy-Efficient and Priority-Aware Task Scheduling for Dynamic Vehicular Edge Computing

Journal Article IEEE Transactions on Intelligent Vehicles · January 1, 2024 The increasing complexity of vehicles has led to a growing demand for in-vehicle services that rely on multiple sensors. In the Vehicular Edge Computing (VEC) paradigm, energy-efficient task scheduling is critical to achieving optimal completion time and e ... Full text Cite

PAG: Protecting Artworks from Personalizing Image Generative Models

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2024 Recent advances in conditional image generation have led to powerful personalized generation models that generate high-resolution artistic images based on simple text descriptions through tuning. However, the abuse of personalized generation models may als ... Full text Cite

Zero-Shot Medical Information Retrieval via Knowledge Graph Embedding

Chapter · January 1, 2024 In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making. This paper introduces MedFusionRank, a novel approach to zero-shot medical information retrieval (MIR) th ... Full text Cite

ES-GNN: Generalizing Graph Neural Networks Beyond Homophily With Edge Splitting

Journal Article IEEE Transactions on Pattern Analysis and Machine Intelligence · January 1, 2024 While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However, real-world networks typically exhibit both homophilic and heterophilic lin ... Full text Cite

Class Incremental Learning for Character String Recognition

Chapter · January 1, 2024 Character string recognition (CSR) has drawn much attention for document intelligence, but its performance is limited by the pre-defined character set without the ability to recognize new characters. To overcome this issue, class incremental learning (CIL) ... Full text Cite

Coarse-to-Fine Document Image Registration for Dewarping

Chapter · January 1, 2024 Document dewarping has made great progress in recent years, however it usually requires huge document pairs with pixel-level annotation to learn a mapping function. Although photographed document images are easy to obtain, the pixel-level annotation betwee ... Full text Cite

HDMTK: Full Integration of Hierarchical Decision-Making and Tactical Knowledge in Multi-Agent Adversarial Games

Journal Article IEEE Transactions on Cognitive and Developmental Systems · January 1, 2024 In the field of adversarial games, existing decision-making algorithms primarily rely on reinforcement learning, which can theoretically adapt to diverse scenarios through trial and error. However, these algorithms often face the challenges of low effectiv ... Full text Cite

Distillation-Based Domain Generalization for Cross-Dataset EEG-Based Emotion Recognition

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · January 1, 2024 Electroencephalogram (EEG)-based emotion recognition has gradually become a research hotspot with extensive real-world applications. Differences in EEG signals across subjects usually lead to the unsatisfactory performance in subject-independent emotion re ... Full text Cite

Semantic Similarity Distance: Towards better text-image consistency metric in text-to-image generation

Journal Article Pattern Recognition · December 1, 2023 Generating high-quality images from text remains a challenge in visual-language understanding, with text-image consistency being a major concern. Particularly, the most popular metric R-precision may not accurately reflect the text-image consistency, leadi ... Full text Cite

A Symbolic Characters Aware Model for Solving Geometry Problems

Conference MM 2023 - Proceedings of the 31st ACM International Conference on Multimedia · October 26, 2023 AI has made significant progress in solving math problems, but geometry problems remain challenging due to their reliance on both text and diagrams. In the text description, symbolic characters such as "ABC"often serve as a bridge to connect the correspond ... Full text Cite

Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation

Conference International Conference on Information and Knowledge Management, Proceedings · October 21, 2023 In domain adaptive segmentation, domain shift may cause erroneous high-confidence predictions on the target domain, resulting in poor self-training. To alleviate the potential error, most previous works mainly consider aleatoric uncertainty arising from th ... Full text Cite

FastAdaBelief: Improving Convergence Rate for Belief-Based Adaptive Optimizers by Exploiting Strong Convexity.

Journal Article IEEE transactions on neural networks and learning systems · September 2023 AdaBelief, one of the current best optimizers, demonstrates superior generalization ability over the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in which it has a data-depende ... Full text Cite

Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation.

Journal Article IEEE journal of biomedical and health informatics · July 2023 Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target doma ... Full text Cite

Explainable Tensorized Neural Ordinary Differential Equations for Arbitrary-Step Time Series Prediction

Journal Article IEEE Transactions on Knowledge and Data Engineering · June 1, 2023 In this work, we propose a continuous neural network architecture, referred to as Explainable Tensorized Neural - Ordinary Differential Equations (ETN-ODE) network for multi-step time series prediction at arbitrary time points. Unlike existing approaches w ... Full text Cite

Towards Simple and Accurate Human Pose Estimation With Stair Network

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · June 1, 2023 In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcome this ... Full text Cite

Fitting Imbalanced Uncertainties in Multi-output Time Series Forecasting

Journal Article ACM Transactions on Knowledge Discovery from Data · May 4, 2023 We focus on multi-step ahead time series forecasting with the multi-output strategy. From the perspective of multi-task learning (MTL), we recognize imbalanced uncertainties between prediction tasks of different future time steps. Unexpectedly, trained by ... Full text Cite

Randomized block-coordinate adaptive algorithms for nonconvex optimization problems

Journal Article Engineering Applications of Artificial Intelligence · May 1, 2023 Nonconvex optimization problems have always been one focus in deep learning, in which many fast adaptive algorithms based on momentum are applied. However, the full gradient computation of high-dimensional feature vector in the above tasks become prohibiti ... Full text Cite

Generalized image outpainting with U-transformer.

Journal Article Neural networks : the official journal of the International Neural Network Society · May 2023 In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalized image outpainting problems. Different from most present image outpainting methods conducting horizontal extrapolation, our genera ... Full text Cite

Graph Neural Networks with Diverse Spectral Filtering

Conference ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 · April 30, 2023 Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts. Despite the success, ... Full text Cite

Towards Faster Training Algorithms Exploiting Bandit Sampling From Convex to Strongly Convex Conditions

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · April 1, 2023 The training process for deep learning and pattern recognition normally involves the use of convex and strongly convex optimization algorithms such as AdaBelief and SAdam to handle lots of 'uninformative' samples that should be ignored, thus incurring extr ... Full text Cite

Machine Learning Methods in Skin Disease Recognition: A Systematic Review

Journal Article Processes · April 1, 2023 Skin lesions affect millions of people worldwide. They can be easily recognized based on their typically abnormal texture and color but are difficult to diagnose due to similar symptoms among certain types of lesions. The motivation for this study is to co ... Full text Cite

Rebalanced Zero-Shot Learning.

Journal Article IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · January 2023 Zero-shot learning (ZSL) aims to identify unseen classes with zero samples during training. Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes. H ... Full text Cite

GSL-VO: A Geometric-Semantic Information Enhanced Lightweight Visual Odometry in Dynamic Environments

Journal Article IEEE Transactions on Instrumentation and Measurement · January 1, 2023 Recently, learning-based visual odometry (VO) has attained remarkable success in vision-based measurement, especially in indoor robotics. Unfortunately, existing methods usually underexplore geometric-semantic (G-S) information, thus resulting in inefficie ... Full text Cite

Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition

Journal Article ACM Transactions on Multimedia Computing, Communications and Applications · October 6, 2022 Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Specifica ... Full text Cite

A Novel 3D Unsupervised Domain Adaptation Framework for Cross-Modality Medical Image Segmentation.

Journal Article IEEE journal of biomedical and health informatics · October 2022 We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT). Previ ... Full text Cite

Zero-Shot Text Classification via Knowledge Graph Embedding for Social Media Data

Journal Article IEEE Internet of Things Journal · June 15, 2022 The idea of 'citizen sensing' and 'human as sensors' is crucial for social Internet of Things, an integral part of cyber-physical-social systems (CPSSs). Social media data, which can be easily collected from the social world, has become a valuable resource ... Full text Cite

LightAdam: Towards a Fast and Accurate Adaptive Momentum Online Algorithm

Journal Article Cognitive Computation · March 1, 2022 Adaptive optimization algorithms enjoy fast convergence and have been widely exploited in pattern recognition and cognitively-inspired machine learning. These algorithms may however be of high computational cost and low generalization ability due to their ... Full text Cite

Analyzing Cell-Scaffold Interaction through Unsupervised 3D Nuclei Segmentation.

Journal Article International journal of bioprinting · January 2022 Fibrous scaffolds have been extensively used in three-dimensional (3D) cell culture systems to establish in vitro models in cell biology, tissue engineering, and drug screening. It is a common practice to characterize cell behaviors on such scaffold ... Full text Cite

Disentangling Semantic-to-Visual Confusion for Zero-Shot Learning

Journal Article IEEE Transactions on Multimedia · January 1, 2022 Using generative models to synthesize visual features from semantic distribution is one of the most popular solutions to ZSL image classification in recent years. The triplet loss (TL) is popularly used to generate realistic visual distributions from seman ... Full text Cite

Outpainting by Queries

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2022 Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent inductive biases to achieve effective sample learning, which may degrade th ... Full text Cite

Artificial Intelligence in Collaborative Computing

Journal Article Mobile Networks and Applications · December 1, 2021 Full text Cite

Coarse-grained generalized zero-shot learning with efficient self-focus mechanism

Journal Article Neurocomputing · November 6, 2021 For image classification in computer vision, the performance of conventional deep neural networks (DNN) may usually drop when labeled training samples are limited. In this case, few-shot learning (FSL) or particularly zero-shot learning (ZSL), i.e. classif ... Full text Cite

Scaffold-A549: A Benchmark 3D Fluorescence Image Dataset for Unsupervised Nuclei Segmentation

Journal Article Cognitive Computation · November 1, 2021 A general trend of nuclei segmentation is the transition from two-dimensional to three-dimensional nuclei segmentation and from traditional image processing methods to data-driven cognitively inspired methods. Existing nuclei segmentation datasets do not m ... Full text Cite

Improving generative adversarial networks with simple latent distributions

Journal Article Neural Computing and Applications · October 1, 2021 Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerful models to generate high-quality images. Although GANs have achieved great success, they usually suffer from unstable training and consequently may lead t ... Full text Cite

Multi-modal generative adversarial networks for traffic event detection in smart cities

Journal Article Expert Systems with Applications · September 1, 2021 Advances in the Internet of Things have enabled the development of many smart city applications and expert systems that help citizens and authorities better understand the dynamics of the cities, and make better planning and utilisation of city resources. ... Full text Cite

Manifold adversarial training for supervised and semi-supervised learning.

Journal Article Neural networks : the official journal of the International Neural Network Society · August 2021 We propose a new regularization method for deep learning based on the manifold adversarial training (MAT). Unlike previous regularization and adversarial training methods, MAT further considers the local manifold of latent representations. Specifically, MA ... Full text Cite

Style-Neutralized Pattern Classification Based on Adversarially Trained Upgraded U-Net

Journal Article Cognitive Computation · July 1, 2021 Traditional machine learning approaches usually hold the assumption that data for model training and in real applications are created following the identical and independent distribution (i.i.d.). However, several relevant research topics have demonstrated ... Full text Cite

Domain adaptation with feature and label adversarial networks

Journal Article Neurocomputing · June 7, 2021 Learning a cross-domain representation from labeled source domains to unlabeled target domains is an important research problem in representation learning. Despite the success of traditional adversarial methods, they proposed to align features from each do ... Full text Cite

Real-time Modeling of Photovoltaic Strings under Partial Shading Conditions

Conference Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021 · May 14, 2021 Partial shading is unavoidable in photovoltaic (PV) systems. However, there still lacks an abstract modeling and simulation for PV strings under partial shading conditions. This paper proposes a modified Tubo search algorithm to find the locations of turni ... Full text Cite

Automated Social Text Annotation With Joint Multilabel Attention Networks.

Journal Article IEEE transactions on neural networks and learning systems · May 2021 Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing ... Full text Cite

Attention-Augmented Machine Memory

Journal Article Cognitive Computation · May 1, 2021 Attention mechanism plays an important role in the perception and cognition of human beings. Among others, many machine learning models have been developed to memorize the sequential data, such as the Long Short-Term Memory (LSTM) network and its extension ... Full text Cite

Attacking Sequential Learning Models with Style Transfer Based Adversarial Examples

Conference Journal of Physics: Conference Series · April 27, 2021 In the field of deep neural network security, it has been recently found that non-sequential networks are vulnerable to adversarial examples. There are however few studies to investigate the adversarial attack on sequential tasks. To this end, in this pape ... Full text Cite

High-Resolution Virtual Try-On Network with Coarse-to-Fine Strategy

Conference Journal of Physics: Conference Series · April 27, 2021 In this paper, we propose a high-resolution virtual try-on network model based on 2D images, which can seamlessly put on given clothing to a target person with any pose. Under the coarse-to-fine strategy, we firstly transform the given normal clothes to wa ... Full text Cite

Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers

Journal Article Expert Systems with Applications · March 1, 2021 Artificial Immune Networks (AIN) is a population-based evolutionary algorithm that is inspired by theoretical immunology. It applies ideas and metaphors from the biological immune system to solve multi-disciplinary problems. This paper presents a novel app ... Full text Cite

State Primitive Learning to Overcome Catastrophic Forgetting in Robotics

Journal Article Cognitive Computation · March 1, 2021 People can learn continuously a wide range of tasks without catastrophic forgetting. To mimic this functioning of continual learning, current methods mainly focus on studying a one-step supervised learning problem, e.g., image classification. They aim to r ... Full text Cite

A Multipath Fusion Strategy Based Single Shot Detector.

Journal Article Sensors (Basel, Switzerland) · February 2021 Object detection has wide applications in intelligent systems and sensor applications. Compared with two stage detectors, recent one stage counterparts are capable of running more efficiently with comparable accuracy, which satisfy the requirement of real- ... Full text Cite

Residual attention-based multi-scale script identification in scene text images

Journal Article Neurocomputing · January 15, 2021 Script identification is an essential step in the text extraction pipeline for multi-lingual application. This paper presents an effective approach to identify scripts in scene text images. Due to the complicated background, various text styles, character ... Full text Cite

A Systematic Analysis of Link Prediction in Complex Network

Journal Article IEEE Access · January 1, 2021 Link mining is an important task in the field of data mining and has numerous applications in informal community. Suppose a real-world complex network, the responsibility of this function is to anticipate those links which are not occurred yet in the given ... Full text Cite

A Segment-Based Layout Aware Model for Information Extraction on Document Images

Conference Communications in Computer and Information Science · January 1, 2021 Information extraction (IE) on document images has attracted considerable attention recently due to its great potentials for intelligent document analysis, where visual layout information is vital. However, most existing works mainly consider visual layout ... Full text Cite

A Covert Ultrasonic Phone-to-Phone Communication Scheme

Conference Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST · January 1, 2021 Smartphone ownership has increased rapidly over the past decade, and the smartphone has become a popular technological product in modern life. The universal wireless communication scheme on smartphones leverages electromagnetic wave transmission, where the ... Full text Cite

Inductive Generalized Zero-Shot Learning with Adversarial Relation Network

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2021 We consider the inductive Generalized Zero Shot Learning (GZSL) problem where test information is assumed unavailable during training. In lack of training samples and attributes for unseen classes, most existing GZSL methods tend to classify target samples ... Full text Cite

Preface

Book · January 1, 2021 Cite

Mix-Up Augmentation for Oracle Character Recognition with Imbalanced Data Distribution

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2021 Oracle bone characters are probably the oldest hieroglyphs in China. It is of significant impact to recognize such characters since they can provide important clues for Chinese archaeology and philology. Automatic oracle bone character recognition however ... Full text Cite

Neural CAPTCHA networks

Journal Article Applied Soft Computing Journal · December 1, 2020 To protect against attacks by malicious computer programs, many websites apply the CAPTCHA (short for completely automated public turing test to tell computers and humans apart) technique for security protection. The distortion, rotation and deformation of ... Full text Cite

Adversarial Domain Adaptation for Crisis Data Classification on Social Media

Conference Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020 · November 1, 2020 Smart cities are cyber-physical-social systems, where city data from different sources could be collected, processed and analyzed to extract useful knowledge. As the volume of data from the social world is exploding, social media data analysis has become a ... Full text Cite

Generative adversarial classifier for handwriting characters super-resolution

Journal Article Pattern Recognition · November 1, 2020 Generative Adversarial Networks (GAN) receive great attention recently due to its excellent performance in image generation, transformation, and super-resolution. However, less emphasis or study has been put on GAN for classification with super-resolution. ... Full text Cite

Pay Attention Selectively and Comprehensively: Pyramid Gating Network for Human Pose Estimation without Pre-training

Conference MM 2020 - Proceedings of the 28th ACM International Conference on Multimedia · October 12, 2020 Deep neural network with multi-scale feature fusion has achieved great success in human pose estimation. However, drawbacks still exist in these methods: 1) they consider multi-scale features equally, which may over-emphasize redundant features; 2) preferr ... Full text Cite

Compressing Deep Networks by Neuron Agglomerative Clustering.

Journal Article Sensors (Basel, Switzerland) · October 2020 In recent years, deep learning models have achieved remarkable successes in various applications, such as pattern recognition, computer vision, and signal processing. However, high-performance deep architectures are often accompanied by a large storage spa ... Full text Cite

Improving deep neural network performance by integrating kernelized Min-Max objective

Journal Article Neurocomputing · September 30, 2020 Deep neural networks (DNN), such as convolutional neural networks (CNN) have been widely used for object recognition. However, they are usually unable to ensure the required intra-class compactness and inter-class separability in the kernel space. These ar ... Full text Cite

Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning.

Journal Article Neural networks : the official journal of the International Neural Network Society · September 2020 Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially witho ... Full text Cite

Super-resolving Tiny Faces with Face Feature Vectors

Conference 10th International Conference on Information Science and Technology, ICIST 2020 · September 1, 2020 Most of the current state-of-the-art tiny face super-resolution (SR) methods aim at learning a single one-to-one mapping to super-resolve low-resolution (LR) face images. In contrast with high-resolution (HR) faces images, LR faces images lack fine facial ... Full text Cite

Multi-modal Adversarial Training for Crisis-related Data Classification on Social Media

Conference Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 · September 1, 2020 Social media platforms such as Twitter are increasingly used to collect data of all kinds. During natural disasters, users may post text and image data on social media platforms to report information about infrastructure damage, injured people, cautions an ... Full text Cite

Editorial: Collaborative Computing for Data-Driven Systems

Journal Article Mobile Networks and Applications · August 1, 2020 Full text Cite

Segmentation mask guided end-to-end person search

Journal Article Signal Processing: Image Communication · August 1, 2020 Person search aims to search for a target person among multiple images recorded by multiple surveillance cameras, which faces various challenges from both pedestrian detection and person re-identification. Besides the large intra-class variations owing to ... Full text Cite

Triple loss for hard face detection

Journal Article Neurocomputing · July 20, 2020 Although face detection has been well addressed in the last decades, despite the achievements in recent years, effective detection of small, blurred and partially occluded faces in the wild remains a challenging task. Meanwhile, the trade-off between compu ... Full text Cite

Maximum Power Point Tracking of Photovoltaic Systems Using Deep Q-networks

Conference IEEE International Conference on Industrial Informatics (INDIN) · July 20, 2020 A photovoltaic (PV) generator exhibits nonlinear current-voltage characteristics and its maximum power point varies with incident atmospheric conditions. Therefore, maximum power point tracking (MPPT) control is required to maximize the output power of the ... Full text Cite

Novel deep neural network based pattern field classification architectures.

Journal Article Neural networks : the official journal of the International Neural Network Society · July 2020 Field classification is a new extension of traditional classification frameworks that attempts to utilize consistent information from a group of samples (termed fields). By forgoing the independent identically distributed (i.i.d.) assumption, field classif ... Full text Cite

Generative adversarial networks with decoder-encoder output noises.

Journal Article Neural networks : the official journal of the International Neural Network Society · July 2020 In recent years, research on image generation has been developing very fast. The generative adversarial network (GAN) emerges as a promising framework, which uses adversarial training to improve the generative ability of its generator. However, since GAN a ... Full text Cite

Knowledge base enrichment by relation learning from social tagging data

Journal Article Information Sciences · July 1, 2020 There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and am ... Full text Cite

Hybrid channel based pedestrian detection

Journal Article Neurocomputing · May 14, 2020 Pedestrian detection has achieved great improvements with the help of Convolutional Neural Networks (CNNs). CNN can learn high-level features from input images, but the insufficient spatial resolution of CNN feature channels (feature maps) may cause a loss ... Full text Cite

Generative adversarial networks with mixture of t-distributions noise for diverse image generation.

Journal Article Neural networks : the official journal of the International Neural Network Society · February 2020 Image generation is a long-standing problem in the machine learning and computer vision areas. In order to generate images with high diversity, we propose a novel model called generative adversarial networks with mixture of t-distributions noise (tGANs). I ... Full text Cite

Correlation Filter Selection for Visual Tracking Using Reinforcement Learning

Journal Article IEEE Transactions on Circuits and Systems for Video Technology · January 1, 2020 Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter-based models are susceptible to wrong updates ... Full text Cite

CDMC’19—The 10th International Cybersecurity Data Mining Competition

Chapter · January 1, 2020 CDMC-International Cybersecurity Data Mining Competition (http://www.csmining.org) is a world unique data-analytic competition sitting in the trans-disciplinary area of artificial intelligence and cybersecurity. In this paper, we summarize CDMC’19—the 10th ... Full text Cite

Feature Representation Matters: End-to-End Learning for Reference-Based Image Super-Resolution

Chapter · January 1, 2020 In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Instead, we only intend to transfer the ... Full text Cite

Improving image caption performance with linguistic context

Chapter · January 1, 2020 Image caption aims to generate a description of an image by using techniques of computer vision and natural language processing, where the framework of Convolutional Neural Networks (CNN) followed by Recurrent Neural Networks (RNN) or particularly LSTM, is ... Full text Cite

Adversarial Rectification Network for Scene Text Regularization

Chapter · January 1, 2020 Scene text recognition with irregular layouts is a challenging yet important problem in computer vision. One widely used method is to employ a rectification network before the recognition stage. However, most previous rectification methods either did not c ... Full text Cite

Feature Redirection Network for Few-Shot Classification

Chapter · January 1, 2020 Few-shot classification aims to learn novel categories by giving few labeled samples. How to make best use of the limited data to obtain a learner with fast learning ability has become a challenging problem. In this paper, we propose a feature redirection ... Full text Cite

Action recognition in videos with temporal segments fusions

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2020 Deep Convolutional Neural Networks (CNNs) have achieved great success in object recognition. However, they are difficult to capture the long-range temporal information, which plays an important role for action recognition in videos. To overcome this issue, ... Full text Cite

Automatic Design of Deep Networks with Neural Blocks

Journal Article Cognitive Computation · January 1, 2020 In recent years, deep neural networks (DNNs) have achieved great successes in many areas, such as cognitive computation, pattern recognition, and computer vision. Although many hand-crafted deep networks have been proposed in the literature, designing a we ... Full text Cite

Fine-grained image classification with object-part model

Chapter · January 1, 2020 Fine-grained image classification is used to identify dozens or hundreds of subcategory images which are classified in a same large category. This task is challenging due to the subtle inter-class visual differences. Most existing methods try to locate dis ... Full text Cite

Improving disentanglement-based image-to-image translation with feature joint block fusion

Chapter · January 1, 2020 Image-to-image translation aims to change attributes or domains of images, where the feature disentanglement based method is widely used recently due to its feasibility and effectiveness. In this method, a feature extractor is usually integrated in the enc ... Full text Cite

Long short-term attention

Chapter · January 1, 2020 Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning. However, although many machine learning models can remember information of data, they have no the attention ... Full text Cite

Offline arabic handwriting recognition using deep machine learning: A review of recent advances

Chapter · January 1, 2020 In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, application for ... Full text Cite

Self-focus deep embedding model for coarse-grained zero-shot classification

Chapter · January 1, 2020 Zero-shot learning (ZSL), i.e. classifying patterns where there is a lack of labeled training data, is a challenging yet important research topic. One of the most common ideas for ZSL is to map the data (e.g., images) and semantic attributes to the same em ... Full text Cite

Multi-scale Attention Consistency for Multi-label Image Classification

Chapter · January 1, 2020 Human has well demonstrated its cognitive consistency over image transformations such as flipping and scaling. In order to learn from human’s visual perception consistency, researchers find out that convolutional neural network’s capacity of discernment ca ... Full text Cite

MCRN: A New Content-Based Music Classification and Recommendation Network

Conference Communications in Computer and Information Science · January 1, 2020 Music classification and recommendation have received wide-spread attention in recent years. However, content-based deep music classification approaches are still very rare. Meanwhile, existing music recommendation systems generally rely on collaborative f ... Full text Cite

Towards better forecasting by fusing near and distant future visions

Conference AAAI 2020 - 34th AAAI Conference on Artificial Intelligence · January 1, 2020 Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different t ... Full text Cite

Reliability does matter: An end-to-end weakly supervised semantic segmentation approach

Conference AAAI 2020 - 34th AAAI Conference on Artificial Intelligence · January 1, 2020 Weakly supervised semantic segmentation is a challenging task as it only takes image-level information as supervision for training but produces pixel-level predictions for testing. To address such a challenging task, most recent state-of-the-art approaches ... Cite

A Novel Deep Density Model for Unsupervised Learning

Journal Article Cognitive Computation · December 1, 2019 Density models are fundamental in machine learning and have received a widespread application in practical cognitive modeling tasks and learning problems. In this work, we introduce a novel deep density model, referred to as deep mixtures of factor analyze ... Full text Cite

Context-aware human activity and smartphone position-mining with motion sensors

Journal Article Remote Sensing · November 1, 2019 Today's smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e. ... Full text Cite

SimpleGAN: Stabilizing generative adversarial networks with simple distributions

Conference IEEE International Conference on Data Mining Workshops, ICDMW · November 1, 2019 Generative Adversarial Networks (GANs) are powerful generative models, but usually suffer from hard training and poor generation. Due to complex data and generation distributions in high dimensional space, it is difficult to measure the departure of two di ... Full text Cite

Random features and random neurons for brain-inspired big data analytics

Conference IEEE International Conference on Data Mining Workshops, ICDMW · November 1, 2019 With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In many real-time applications, it is preferable to be able to process more data with ... Full text Cite

Generalized adversarial training in riemannian space

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · November 1, 2019 Adversarial examples, referred to as augmented data points generated by imperceptible perturbations of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep neural network (DNN) models t ... Full text Cite

Deep minimax probability machine

Conference IEEE International Conference on Data Mining Workshops, ICDMW · November 1, 2019 Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Althou ... Full text Cite

Primitives generation policy learning without catastrophic forgetting for robotic manipulation

Conference IEEE International Conference on Data Mining Workshops, ICDMW · November 1, 2019 Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual ... Full text Cite

VSB-DVM: An end-to-end bayesian nonparametric generalization of deep variational mixture model

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · November 1, 2019 Mixture of factor analyzers is a fundamental model in unsupervised learning, which is particularly useful for high dimensional data. Recent efforts on deep auto-encoding mixture models made a fruitful progress in clustering. However, in most cases, their p ... Full text Cite

Beyond attributes: High-order attribute features for zero-shot learning

Conference Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 · October 1, 2019 In this paper, SeeNet with the high-order attribute features (SeeNet-HAF) is proposed to solve the challenging zero-shot learning (ZSL) task. The high-order attribute features aims to discover a more elaborate, discriminative high-order semantic vector for ... Full text Cite

Cross-modality interactive attention network for multispectral pedestrian detection

Journal Article Information Fusion · October 1, 2019 Multispectral pedestrian detection is an emerging solution with great promise in many around-the-clock applications, such as automotive driving and security surveillance. To exploit the complementary nature and remedy contradictory appearance between modal ... Full text Cite

An interactive and generative approach for Chinese Shanshui painting document

Conference Proceedings of the International Conference on Document Analysis and Recognition, ICDAR · September 1, 2019 Chinese Shanshui is a landscape painting document mainly drawing mountain and water, which is popular in Chinese culture. However, it is very challenging to create this by general people. In this paper, we propose an interactive and generative approach to ... Full text Cite

Discriminant Zero-Shot Learning with Center Loss

Journal Article Cognitive Computation · August 15, 2019 Current work on zero-shot learning (ZSL) generally does not focus on the discriminative ability of the models, which is important for differentiating between classes since our brain focuses on the discriminating part of the object to classify it. For gener ... Full text Cite

MPSSD: Multi-Path Fusion Single Shot Detector

Conference Proceedings of the International Joint Conference on Neural Networks · July 1, 2019 Recent prevalent one stage detectors, such as single shot detector (SSD) and RetinaNet, are able to detect objects faster than two stage ones while maintaining comparable accuracy. To further boost the accuracy, many studies focus on enhancing the multi-sc ... Full text Cite

Stochastic Conjugate Gradient Algorithm With Variance Reduction.

Journal Article IEEE transactions on neural networks and learning systems · May 2019 Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction1 and we prove its linear convergen ... Full text Cite

Special issue on advances in graph algorithm and applications

Journal Article Neurocomputing · April 7, 2019 Full text Cite

Mining human activity and smartphone position from motion sensors

Conference International Conference on Intelligent User Interfaces, Proceedings IUI · March 16, 2019 The wide use of motion sensors in today's smartphones has enabled a range of innovative applications which these sensors are not originally designed for. Human activity recognition and smartphone position detection are two of them. In this paper, we presen ... Full text Cite

Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios

Journal Article IEEE Transactions on Industry Applications · March 1, 2019 Partial shading is an unavoidable complication in the field of photovoltaic (PV) generation. Bypass diodes have become a standard feature of solar cell arrays to improve array performance under partial shading scenarios (PSS). However, the current-voltage ... Full text Cite

IAN: The Individual Aggregation Network for Person Search

Journal Article Pattern Recognition · March 1, 2019 Person search in real-world scenarios is a new challenging computer version task with many meaningful applications. The challenge of this task mainly comes from: (1) unavailable bounding boxes for pedestrians and the model needs to search for the person ov ... Full text Cite

Guided policy search for sequential multitask learning

Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems · January 1, 2019 Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy se ... Full text Cite

Enhanced LSTM with batch normalization

Chapter · January 1, 2019 Recurrent neural networks (RNNs) are powerful models for sequence learning. However, the training of RNNs is complicated because the internal covariate shift problem, where the input distribution at each iteration changes during the training as the paramet ... Full text Cite

Joint multi-label attention networks for social text annotation

Conference NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference · January 1, 2019 We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, an ... Cite

Learning Latent Features with Infinite Nonnegative Binary Matrix Trifactorization

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · December 1, 2018 Nonnegative matrix factorization (NMF) has been widely exploited in many computational intelligence and pattern recognition problems. In particular, it can be used to extract latent features from data. However, previous NMF models often assume a fixed numb ... Full text Cite

Accelerating Infinite Ensemble of Clustering by Pivot Features

Journal Article Cognitive Computation · December 1, 2018 The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system ... Full text Cite

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

Journal Article Expert Systems with Applications · December 1, 2018 In this paper, we present a novel feature extraction technique, termed Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH), and exploit it to detect breast cancer in volumetric medical images. The technique is incorporated as part of an intellig ... Full text Cite

A new two-layer mixture of factor analyzers with joint factor loading model for the classification of small dataset problems

Journal Article Neurocomputing · October 27, 2018 Dimensionality Reduction (DR) is a fundamental topic of pattern classification and machine learning. For classification tasks, DR is typically employed as a pre-processing step, succeeded by an independent classifier training stage. However, such independe ... Full text Cite

Banzhaf random forests: Cooperative game theory based random forests with consistency.

Journal Article Neural networks : the official journal of the International Neural Network Society · October 2018 Random forests algorithms have been widely used in many classification and regression applications. However, the theory of random forests lags far behind their applications. In this paper, we propose a novel random forests classification algorithm based on ... Full text Cite

Approximately optimizing NDCG using pair-wise loss

Journal Article Information Sciences · July 1, 2018 The Normalized Discounted Cumulative Gain (NDCG) is used to measure the performance of ranking algorithms. Much of the work on learning to rank by optimizing NDCG directly or indirectly is based on list-wise approaches. In our work, we approximately optimi ... Full text Cite

Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification

Journal Article Cognitive Computation · February 1, 2018 In order to extract effective representations of data using deep learning models, deep convolutional activation feature (DeCAF) is usually considered. However, since the deep models for learning DeCAF are generally pre-trained, the dimensionality of DeCAF ... Full text Cite

Learning from Few Samples with Memory Network

Journal Article Cognitive Computation · February 1, 2018 Neural networks (NN) have achieved great successes in pattern recognition and machine learning. However, the success of a NN usually relies on the provision of a sufficiently large number of data samples as training data. When fed with a limited data set, ... Full text Cite

Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery

Journal Article Cognitive Computation · February 1, 2018 Full text Cite

Zero-Shot Learning via Attribute Regression and Class Prototype Rectification.

Journal Article IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · February 2018 Zero-shot learning (ZSL) aims at classifying examples for unseen classes (with no training examples) given some other seen classes (with training examples). Most existing approaches exploit intermedia-level information (e.g., attributes) to transfer knowle ... Full text Cite

Siamese network ensemble for visual tracking

Journal Article Neurocomputing · January 31, 2018 Visual object tracking is a challenging task considering illumination variation, occlusion, rotation, deformation and other problems. In this paper, we extend a Siamese INstance search Tracker (SINT) with model updating mechanism to improve its tracking ro ... Full text Cite

W-Net: One-shot arbitrary-style chinese character generation with deep neural networks

Chapter · January 1, 2018 Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficien ... Full text Cite

Improving deep neural network performance with kernelized min-max objective

Chapter · January 1, 2018 In this paper, we present a novel training strategy using kernelized Min-Max objective to enable improved object recognition performance on deep neural networks (DNN), e.g., convolutional neural networks (CNN). Without changing the other part of the origin ... Full text Cite

Style Neutralization Generative Adversarial Classifier

Chapter · January 1, 2018 Breathtaking improvement has been seen with the recently proposed deep Generative Adversarial Network (GAN). Purposes of most existing GAN-based models majorly concentrate on generating realistic and vivid patterns by a pattern generator with the aid of th ... Full text Cite

Fast graph-based semi-supervised learning and its applications

Chapter · January 1, 2018 Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this chapter, we propose an efficient and robust graph-based transductive classification method, called minimum tre ... Cite

Semi-supervised learning: Background, applications and future directions

Book · January 1, 2018 Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very scarce labeled data. The book focuses on state-of-the-art research on semi-supervised learning. In the first chapter, We ... Cite

Self-training field pattern prediction based on kernel methods

Chapter · January 1, 2018 Conventional predictors often regard input samples as identically and independently distributed (i.i.d.). Such an assumption does not always hold in many real scenarios, especially when patterns occur as groups, where each group shares a homogeneous style. ... Cite

Preface

Book · January 1, 2018 Cite

Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance

Journal Article IEEE Transactions on Circuits and Systems I: Regular Papers · December 1, 2017 An accurate measurement of the solar irradiance is of significance for evaluating and developing of solar renewable energy systems. Soft sensors are used to provide feasible and economical alternatives to costly physical measurement instruments (e.g., pyra ... Full text Cite

Field Support Vector Machines

Journal Article IEEE Transactions on Emerging Topics in Computational Intelligence · December 1, 2017 Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such task ... Full text Cite

Integrated discovery of location prediction rules in mobile environment

Conference Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017 · July 2, 2017 Pattern-based prediction is one of the widely used approaches to predict the future location of the users in a mobile environment. Currently, pattern-based prediction is performed in two sequential steps: discovering a set of sequential frequent patterns, ... Full text Cite

Joint Learning of Unsupervised Dimensionality Reduction and Gaussian Mixture Model

Journal Article Neural Processing Letters · June 1, 2017 Dimensionality reduction (DR) has been one central research topic in information theory, pattern recognition, and machine learning. Apparently, the performance of many learning models significantly rely on dimensionality reduction: successful DR can largel ... Full text Cite

Customer churn prediction in the telecommunication sector using a rough set approach

Journal Article Neurocomputing · May 10, 2017 Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitioners, ... Full text Cite

Lung cancer detection using Local Energy-based Shape Histogram (LESH) feature extraction and cognitive machine learning techniques

Conference Proceedings of 2016 IEEE 15th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2016 · February 21, 2017 The novel application of Local Energy-based Shape Histogram (LESH) feature extraction technique was recently proposed for breast cancer diagnosis using mammogram images [22]. This paper extends our original work to apply the LESH technique to detect lung c ... Full text Cite

Improve deep learning with unsupervised objective

Chapter · January 1, 2017 We propose a novel approach capable of embedding the unsupervised objective into hidden layers of the deep neural network (DNN) for preserving important unsupervised information. To this end, we exploit a very simple yet effective unsupervised method, i.e. ... Full text Cite

Field support vector regression

Chapter · January 1, 2017 In regression tasks for static data, existing methods often assume that they were generated from an identical and independent distribution (i.i.d.). However, violation can be found when input samples may form groups, each affected by a certain different do ... Full text Cite

Deep mixtures of factor analyzers with common loadings: A novel deep generative approach to clustering

Chapter · January 1, 2017 In this paper, we propose a novel deep density model, called Deep Mixtures of Factor Analyzers with Common Loadings (DMCFA). Employing a mixture of factor analyzers sharing common component loadings, this novel model is more physically meaningful, since th ... Full text Cite

Statistical entity ranking with domain knowledge

Chapter · December 1, 2016 Entity search is a new application meeting either precise or vague requirements from the search engines users. Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search. We achieved the first place with the average MAP ... Full text Cite

A fast projected fixed-point algorithm for large graph matching

Journal Article Pattern Recognition · December 1, 2016 We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability. By using a new doubly stochastic projection, for matching two wei ... Full text Cite

Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model

Journal Article Applied Soft Computing Journal · March 1, 2016 Bio-inspired metaheuristic algorithms have been widely applied in estimating the extrinsic parameters of a photovoltaic (PV) model. These methods are capable of handling the nonlinearity of objective functions whose derivatives are often not defined as wel ... Full text Cite

A unified gradient regularization family for adversarial examples

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · January 5, 2016 Adversarial examples are augmented data points generated by imperceptible perturbation of input samples. They have recently drawn much attention with the machine learning and data mining community. Being difficult to distinguish from real examples, such ad ... Full text Cite

Learning from few samples with memory network

Chapter · January 1, 2016 Neural Networks (NN) have achieved great success in pattern recognition and machine learning. However, the success of NNs usually relies on a sufficiently large number of samples. When fed with limited data, NN’s performance may be degraded significantly. ... Full text Cite

An investigation of machine learning and neural computation paradigms in the design of clinical decision support systems (CDSSs)

Chapter · January 1, 2016 This paper reviews the state of the art techniques for designing next generation CDSSs. CDSS can aid physicians and radiologists to better analyse and treat patients by combining their respective clinical expertise with complementary capabilities of the co ... Full text Cite

Learning latent features with infinite non-negative binary matrix tri-factorization

Chapter · January 1, 2016 Non-negative Matrix Factorization (NMF) has been widely exploited to learn latent features from data. However, previous NMF models often assume a fixed number of features, say p features, where p is simply searched by experiments. Moreover, it is even diff ... Full text Cite

Maximum margin semi-supervised learning with irrelevant data.

Journal Article Neural networks : the official journal of the International Neural Network Society · October 2015 Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the t ... Full text Cite

DE²: Dynamic ensemble of ensembles for learning nonstationary data

Journal Article Neurocomputing · October 1, 2015 Learning nonstationary data with concept drift has received much attention in machine learning and been an active topic in ensemble learning. Specifically, batch growing ensemble methods present one important direction for dealing with concept drift involv ... Full text Cite

Two-layer Mixture of Factor Analyzers with Joint Factor Loading

Conference Proceedings of the International Joint Conference on Neural Networks · September 28, 2015 Dimensionality Reduction (DR) is a fundamental yet active research topic in pattern recognition and machine learning. When used in classification, previous research usually performs DR separately, and then inputs the reduced features to other available mod ... Full text Cite

MTC: A Fast and Robust Graph-Based Transductive Learning Method.

Journal Article IEEE transactions on neural networks and learning systems · September 2015 Despite the great success of graph-based transductive learning methods, most of them have serious problems in scalability and robustness. In this paper, we propose an efficient and robust graph-based transductive classification method, called minimum tree ... Full text Cite

Learning Imbalanced Classifiers Locally and Globally with One-Side Probability Machine

Journal Article Neural Processing Letters · June 1, 2015 We consider the imbalanced learning problem, where the data associated with one class are far fewer than those associated with the other class. Current imbalanced learning methods often handle this problem by adapting certain intermediate parameters so as ... Full text Cite

WSDM'15 workshop summary / scalable data analytics:Theory and applications

Conference WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining · February 2, 2015 The SDA workshop at WSDM 2015 is the fifth International Workshop on Scalable Data Analytics, following the previous four workshops of SDA respectively held at IEEE Big Data 2013, PAKDD 2014, IEEE Big Data 2014, and IEEE ICDM 2014. This series of workshops ... Full text Cite

Hybrid metaheuristic algorithms: Past, present, and future

Chapter · January 1, 2015 Hybrid algorithms play a prominent role in improving the search capability of algorithms. Hybridization aims to combine the advantages of each algorithm to form a hybrid algorithm, while simultaneously trying to minimize any substantial disadvantage. In ge ... Full text Cite

Is decaf good enough for accurate image classification?

Chapter · January 1, 2015 In recent years, deep learning has attracted much interest for addressing complex AI tasks. However, most of the deep learning models need to be trained for a long time in order to obtain good results. To overcome this problem, the deep convolutional activ ... Full text Cite

Learning locality preserving graph from data.

Journal Article IEEE transactions on cybernetics · November 2014 Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we prop ... Full text Cite

A novel classifier ensemble method with sparsity and diversity

Journal Article Neurocomputing · June 25, 2014 We consider the classifier ensemble problem in this paper. Due to its superior performance to individual classifiers, class ensemble has been intensively studied in the literature. Generally speaking, there are two prevalent research directions on this, i. ... Full text Cite

Robust Text Detection in Natural Scene Images.

Journal Article IEEE transactions on pattern analysis and machine intelligence · May 2014 Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is ... Full text Cite

Graphical lasso quadratic discriminant function and its application to character recognition

Journal Article Neurocomputing · April 10, 2014 Multivariate Gaussian distribution is a popular assumption in many pattern recognition tasks. The quadratic discriminant function (QDF) is an effective classification approach based on this assumption. An improved algorithm, called modified QDF (or MQDF in ... Full text Cite

A novel hybrid approach for combining deep and traditional neural networks

Chapter · January 1, 2014 Over last fifty years, Neural Networks (NN) have been important and active models in machine learning and pattern recognition. Among different types of NNs, Back Propagation (BP) NN is one popular model, widely exploited in various applications. Recently, ... Full text Cite

Text categorization with diversity random forests

Chapter · January 1, 2014 Text categorization (TC), has many typical traits, such as large and difficult category taxonomies, noise and incremental data, etc. Random Forests, one of the most important but simple state-of-the-art ensemble methods, has been used to solve such type of ... Full text Cite

Preface

Book · January 1, 2014 Full text Cite

Convex ensemble learning with sparsity and diversity

Journal Article Information Fusion · January 1, 2014 Classifier ensemble has been broadly studied in two prevalent directions, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. While most current approaches are emphasized on either sparsity or diversity only, we ... Full text Cite

Combination of classification and clustering results with label propagation

Journal Article IEEE Signal Processing Letters · January 1, 2014 This letter considers the combination of multiple classification and clustering results to improve the prediction accuracy. First, an object-similarity graph is constructed from multiple clustering results. The labels predicted by the classification models ... Full text Cite

Unsupervised dimensionality reduction for gaussian mixture model

Chapter · January 1, 2014 Dimensionality reduction is a fundamental yet active research topic in pattern recognition and machine learning. On the other hand, Gaussian Mixture Model (GMM), a famous model, has been widely used in various applications, e.g., clustering and classificat ... Full text Cite

Preface

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2014 Cite

Feature transformation with class conditional decorrelation

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2013 The well-known feature transformation model of Fisher linear discriminant analysis (FDA) can be decomposed into an equivalent two-step approach: whitening followed by principal component analysis (PCA) in the whitened space. By proving that whitening is th ... Full text Cite

One-side probability machine: Learning imbalanced classifiers locally and globally

Chapter · December 1, 2013 Imbalanced learning is a challenged task in machine learning, where the data associated with one class are far fewer than those associated with the other class. In this paper, we propose a novel model called One-Side Probability Machine (OSPM) able to lear ... Full text Cite

Dynamic ensemble of ensembles in nonstationary environments

Chapter · December 1, 2013 Classifier ensemble is an active topic for learning from non-stationary data. In particular, batch growing ensemble methods present one important direction for dealing with concept drift involved in non-stationary data. However, current batch growing ensem ... Full text Cite

Fast kNN graph construction with locality sensitive hashing

Chapter · October 31, 2013 The k nearest neighbors (kNN) graph, perhaps the most popular graph in machine learning, plays an essential role for graph-based learning methods. Despite its many elegant properties, the brute force kNN graph construction method has computational complexi ... Full text Cite

Efficient clinical decision making by learning from missing clinical data

Conference Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-Health, CICARE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013 · September 16, 2013 Clinical decision making frequently involves making decisions under uncertainty because of missing key patient data (e.g, demographics, episodic and clinical diagnosis details) - this information is essential for modern clinical decision support systems to ... Full text Cite

Accurate and robust text detection: A step-in for text retrieval in natural scene images

Conference SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval · September 2, 2013 We propose and implement a robust text detection system, which is a prominent step-in for text retrieval in natural scene images or videos. Our system includes several key components: (1) A fast and effective pruning algorithm is designed to extract Maxima ... Full text Cite

Geometry preserving multi-task metric learning

Journal Article Machine Learning · July 1, 2013 In this paper, we consider the multi-task metric learning problem, i.e., the problem of learning multiple metrics from several correlated tasks simultaneously. Despite the importance, there are only a limited number of approaches in this field. While the e ... Full text Cite

A multi-task framework for metric learning with common subspace

Journal Article Neural Computing and Applications · June 1, 2013 Metric learning has been widely studied in machine learning due to its capability to improve the performance of various algorithms. Meanwhile, multi-task learning usually leads to better performance by exploiting the shared information across all tasks. In ... Full text Cite

Local Tangent Space Laplacian Eigenmaps

Chapter · December 1, 2012 This chapter presents a novel manifold learning algorithm, named Local Tangent Space Laplacian Eigenmaps (LTSLE). The theoretical framework of LTSLE is based on a local tangent space theorem, which is also delivered in this chapter. LTSLE ismotivated by th ... Cite

Manifold regularized multi-task learning

Chapter · November 19, 2012 Multi-task learning (MTL) has drawn a lot of attentions in machine learning. By training multiple tasks simultaneously, information can be better shared across tasks. This leads to significant performance improvement in many problems. However, most existin ... Full text Cite

Classifier ensemble using a heuristic learning with sparsity and diversity

Chapter · November 19, 2012 Classifier ensemble has been intensively studied with the aim of overcoming the limitations of individual classifier components in two prevalent directions, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. Cu ... Full text Cite

Multiple Outlooks Learning with Support Vector Machines

Chapter · November 19, 2012 Multiple Outlooks Learning (MOL) has recently received considerable attentions in machine learning. While traditional classification models often assume patterns are living in a fixed-dimensional vector space, MOL focuses on the tasks involving multiple re ... Full text Cite

Geometry preserving multi-task metric learning

Chapter · October 4, 2012 Multi-task learning has been widely studied in machine learning due to its capability to improve the performance of multiple related learning problems. However, few researchers have applied it on the important metric learning problem. In this paper, we pro ... Full text Cite

Maxi-Min discriminant analysis via online learning.

Journal Article Neural networks : the official journal of the International Neural Network Society · October 2012 Linear Discriminant Analysis (LDA) is an important dimensionality reduction algorithm, but its performance is usually limited on multi-class data. Such limitation is incurred by the fact that LDA actually maximizes the average divergence among classes, whe ... Full text Cite

Joint learning of error-correcting output codes and dichotomizers from data

Journal Article Neural Computing and Applications · June 1, 2012 The ECOC technique is a powerful tool to learn and combine multiple binary learners for multi-class classification. It generally involves three steps: coding, dichotomizers learning, and decoding. In previous ECOC methods, the coding step and the dichotomi ... Full text Cite

Fast and robust graph-based transductive learning via minimum tree cut

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2011 In this paper, we propose an efficient and robust algorithm for graph-based transductive classification. After approximating a graph with a spanning tree, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. ... Full text Cite

Preface

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2011 Full text Cite

Low rank metric learning with manifold regularization

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2011 In this paper, we present a semi-supervised method to learn a low rank Mahalanobis distance function. Based on an approximation to the projection distance from a manifold, we propose a novel parametric manifold regularizer. In contrast to previous approach ... Full text Cite

Pattern field classification with style normalized transformation

Conference IJCAI International Joint Conference on Artificial Intelligence · December 1, 2011 Field classification is an extension of the traditional classification framework, by breaking the i.i.d. assumption. In field classification, patterns occur as groups (fields) of homogeneous styles. By utilizing style consistency, classifying groups of pat ... Full text Cite

Graphical lasso quadratic discriminant function for character recognition

Chapter · November 28, 2011 The quadratic discriminant function (QDF) derived from the multivariate Gaussian distribution is effective for classification in many pattern recognition tasks. In particular, a variant of QDF, called MQDF, has achieved great success and is widely recogniz ... Full text Cite

Multi-task low-rank metric learning based on common subspace

Chapter · November 28, 2011 Multi-task learning, referring to the joint training of multiple problems, can usually lead to better performance by exploiting the shared information across all the problems. On the other hand, metric learning, an important research topic, is however ofte ... Full text Cite

FMI image based rock structure classification using classifier combination

Journal Article Neural Computing and Applications · October 1, 2011 Formation Micro Imager (FMI) can directly reflect changes of wall stratums and rock structures, and is an important factor to classify stratums and identify lithology for the oil and gas exploration. Conventionally, people analyze FMI images mainly with ma ... Full text Cite

Exchange rate prediction with non-numerical information

Journal Article Neural Computing and Applications · October 1, 2011 Exchange rate prediction is an important yet challenging problem in financial time series analysis. Although the historical exchange rates can provide valuable information, other factors will also affect the prediction significantly. These factors could be ... Full text Cite

Generalized sparse metric learning with relative comparisons

Journal Article Knowledge and Information Systems · January 1, 2011 The objective of sparse metric learning is to learn a distance measure from a set of data in addition to finding a low-dimensional representation. Despite demonstrated success, the performance of existing sparse metric learning approaches is usually limite ... Full text Cite

Learning ECOC and dichotomizers jointly from data

Chapter · December 21, 2010 In this paper, we present a first study which learns the ECOC matrix as well as dichotomizers simultaneously from data; these two steps are usually conducted independently in previous methods. We formulate our learning model as a sequence of concave-convex ... Full text Cite

Similar handwritten Chinese characters recognition by critical region selection based on average symmetric uncertainty

Conference Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010 · December 1, 2010 We consider the problem of similar Chinese character recognition in this paper. Engaging the Average Symmetric Uncertainty (ASU) criterion to measure the correlation between different image regions and the class label, we manage to detect the most critical ... Full text Cite

Dimensionality reduction by minimal distance maximization

Conference Proceedings - International Conference on Pattern Recognition · November 18, 2010 In this paper, we propose a novel discriminant analysis method, called Minimal Distance Maximization (MDM). In contrast to the traditional LDA, which actually maximizes the average divergence among classes, MDM attempts to find a low-dimensional subspace t ... Full text Cite

Sparse learning for support vector classification

Journal Article Pattern Recognition Letters · October 1, 2010 This paper provides a sparse learning algorithm for Support Vector Classification (SVC), called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by automatically setting the irrelevant parameters exactly to zero. SSVC adopts the ... Full text Cite

Robust metric learning by smooth optimization

Conference Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010 · January 1, 2010 Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit fe ... Cite

GSML: A unified framework for sparse metric learning

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2009 There has been significant recent interest in sparse metric learning (SML) in which we simultaneously learn both a good distance metric and a low-dimensional representation. Unfortunately, the performance of existing sparse metric learning approaches is us ... Full text Cite

Exchange rate forecasting using classifier ensemble

Chapter · December 1, 2009 In this paper, we investigate the impact of the non-numerical information on exchange rate changes and that of ensemble multiple classifiers on forecasting exchange rate between U.S. dollar and Japanese yen. We first engage the fuzzy comprehensive evaluati ... Full text Cite

A rock structure recognition system using FMI images

Chapter · December 1, 2009 Formation Micro Imager (FMI) can directly reflect changes of wall stratum and rock structures. It is also an important method to divide stratum and identify lithology. However, people usually deal with FMI images manually, which is extremely inefficient an ... Full text Cite

Supervised Self-taught Learning: Actively transferring knowledge from unlabeled data

Conference Proceedings of the International Joint Conference on Neural Networks · November 18, 2009 We consider the task of Self-taught Learning (STL) from unlabeled data. In contrast to semi-supervised learning, which requires unlabeled data to have the same set of class labels as labeled data, STL can transfer knowledge from different types of unlabele ... Full text Cite

A novel kernel-based maximum a posteriori classification method.

Journal Article Neural networks : the official journal of the International Neural Network Society · September 2009 Kernel methods have been widely used in pattern recognition. Many kernel classifiers such as Support Vector Machines (SVM) assume that data can be separated by a hyperplane in the kernel-induced feature space. These methods do not consider the data distrib ... Full text Cite

Enhanced protein fold recognition through a novel data integration approach.

Journal Article BMC bioinformatics · August 2009 BackgroundProtein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived ... Full text Cite

Localized support vector regression for time series prediction

Journal Article Neurocomputing · June 1, 2009 Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regressi ... Full text Cite

Arbitrary norm support vector machines.

Journal Article Neural computation · February 2009 Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L(infinity)-norm SVM, are rarely seen in the lit ... Full text Cite

Sparse metric learning via smooth optimization

Conference Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference · January 1, 2009 In this paper we study the problem of learning a low-rank (sparse) distance matrix. We propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixed-norm r ... Cite

Direct zero-norm optimization for feature selection

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2008 Zero-norm, defined as the number of non-zero elements in a vector, is an ideal quantity for feature selection. However, minimization of zero-norm is generally regarded as a combinatorially difficult optimization problem. In contrast to previous methods tha ... Full text Cite

Semi-supervised text categorization by active search

Conference International Conference on Information and Knowledge Management, Proceedings · December 1, 2008 In automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high clas- sification accuracy. To address this problem, a novel web-assisted tex ... Full text Cite

Semi-supervised learning from general unlabeled data

Conference Proceedings - IEEE International Conference on Data Mining, ICDM · December 1, 2008 We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better utilize the information from unlabeled data by formulating them as a three-c ... Full text Cite

Efficient minimax clustering probability machine by generalized probability product kernel

Conference Proceedings of the International Joint Conference on Neural Networks · November 24, 2008 Minimax Probability Machine (MPM), learning a decision function by minimizing the maximum probability of misclassiflcation, has demonstrated very promising performance in classification and regression. However, MPM is often challenged for its slow training ... Full text Cite

Kernel maximum a posteriori classification with error bound analysis

Chapter · October 27, 2008 Kernel methods have been widely used in data classification. Many kernel-based classifiers like Kernel Support Vector Machines (KSVM) assume that data can be separated by a hyperplane in the feature space. These methods do not consider the data distributio ... Full text Cite

A scenario-view based approach to analyze external behavior of web services for supporting mediated service interactions

Conference Proceedings - 2008 IEEE International Conference on Services Computing, SCC 2008 · September 19, 2008 Web service interactions have triggered the initiative to identify and solve mismatches from a behavioral aspect. Current approaches are limited since they mainly focus on control-.ow but largely ignore data-.ow. In this paper, we propose an approach to au ... Full text Cite

Maxi-min margin machine: learning large margin classifiers locally and globally.

Journal Article IEEE transactions on neural networks · February 2008 In this paper, we propose a novel large margin classifier, called the maxi-min margin machine M(4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only eithe ... Full text Cite

Preface

Book · January 1, 2008 Cite

Degraded character recognition by complementary classifiers combination

Conference Proceedings of the International Conference on Document Analysis and Recognition, ICDAR · December 1, 2007 Character degradation is a big problem for machine printed character recognition. Two main reasons for degradation are extrinsic image degradation such as blurring and low image dimension, and intrinsic degradation caused by font variations. A recognition ... Full text Cite

An SVM-based high-accurate recognition approach for handwritten numerals by using difference features

Conference Proceedings of the International Conference on Document Analysis and Recognition, ICDAR · December 1, 2007 Handwritten numeral recognition is an important pattern recognition task. It can be widely used in various domains, e.g., bank money recognition, which requires a very high recognition rate. As a state-of-the-art classifier, Support Vector Machine (SVM), h ... Full text Cite

A novel discriminative naive Bayesian network for classification

Chapter · December 1, 2007 Naive Bayesian network (NB) is a simple yet powerful Bayesian network. Even with a strong independency assumption among the features, it demonstrates competitive performance against other state-of-the-art classifiers, such as support vector machines (SVM). ... Full text Cite

Local support vector regression for financial time series prediction

Conference IEEE International Conference on Neural Networks - Conference Proceedings · December 1, 2006 We consider the regression problem for financial time series. Typically, financial time series are non-stationary and volatile in nature. Because of its good generalization power and the tractability of the problem, the Support Vector Regression (SVR) has ... Cite

Imbalanced learning with a biased minimax probability machine.

Journal Article IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society · August 2006 Imbalanced learning is a challenged task in machine learning. In this context, the data associated with one class are far fewer than those associated with the other class. Traditional machine learning methods seeking classification accuracy over a full ran ... Full text Cite

Maximizing sensitivity in medical diagnosis using biased minimax probability machine.

Journal Article IEEE transactions on bio-medical engineering · May 2006 The challenging task of medical diagnosis based on machine learning techniques requires an inherent bias, i.e., the diagnosis should favor the "ill" class over the "healthy" class, since misdiagnosing a patient as a healthy person may delay the therapy and ... Full text Cite

A hybrid handwritten chinese address recognition approach

Chapter · January 1, 2006 Handwritten Chinese Address Recognition describes a difficult yet important pattern recognition tusk. There are three difficulties in this problem: (1) Handwritten address is often of free styles and of high variations, resulting in inevitable segmentation ... Full text Cite

An efficient post-processing approach for off-line handwritten Chinese address recognition

Conference International Conference on Signal Processing Proceedings, ICSP · January 1, 2006 Language model is widely used in OCR post-processing. In this paper, based on language model, we propose a two-step method for post-processing of off-line handwritten Chinese address recognition. According to the characteristics of high level and low level ... Full text Cite

Learning large margin classifiers locally and globally

Conference Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 · December 1, 2004 A new large margin classifier, named Maxi-Min Margin Machine (M 4) is proposed in this paper. This new classifier is constructed based on both a "local" and a "global" view of data, while the most popular large margin classifier, Support Vector Machine (SV ... Cite

Biased support vector machine for relevance feedback in image retrieval

Conference IEEE International Conference on Neural Networks - Conference Proceedings · December 1, 2004 Recently, Support Vector Machines (SVMs) have been engaged on relevance feedback tasks in content-based image retrieval. Typical approaches by SVMs treat the relevance feedback as a strict binary classification problem. However, these approaches do not con ... Cite

Learning classifiers from imbalanced data based on biased minimax probability machine

Conference Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition · October 19, 2004 We consider the problem of the binary classification on imbalanced data, in which nearly all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. Traditional machine learning ... Cite

The minimum error minimax probability machine

Journal Article Journal of Machine Learning Research · October 1, 2004 We construct a distribution-free Bayes optimal classifier called the Minimum Error Minimax Probability Machine (MEMPM) in a worst-case setting, i.e., under all possible choices of class-conditional densities with a given mean and covariance matrix. By assu ... Cite

Outliers treatment in support vector regression for financial time series prediction

Chapter · January 1, 2004 Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In t ... Full text Cite

Discriminative Training of Bayesian Chow-Liu Multinet Classifiers

Conference Proceedings of the International Joint Conference on Neural Networks · September 24, 2003 Discriminative classifiers such as Support Vector Machines directly learn a discriminant function or a posterior probability model to perform classification. On the other hand, generative classifiers often learn a joint probability model and then use Bayes ... Cite

Finite mixture model of bounded semi-naive bayesian networks classifier

Chapter · January 1, 2003 The Semi-Naive Bayesian network (SNB) classifier, a probabilistic model with an assumption of conditional independence among the combined attributes, shows a good performance in classification tasks. However, the traditional SNBs can only combine two attri ... Full text Cite

Constructing a large node Chow-Liu tree based on frequent itemsets

Conference ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age · January 1, 2002 We present a novel approach to construct a kind of tree belief network, in which the "nodes" are subsets of variables of dataset. We call this large node Chow-Liu tree (LNCLT). Similar to the Chow-Liu tree (1968), the LNCLT is also ideal for density estima ... Full text Cite

Learning maximum likelihood semi-naive Bayesian network classifier

Conference Proceedings of the IEEE International Conference on Systems, Man and Cybernetics · January 1, 2002 In this paper, we propose a technique to construct a sub-optimal semi-naive Bayesian network when given a bound on the maximum number of variables that can be combined into a node. We theoretically show that our approach has a less computation cost when co ... Cite