Kaizhu Huang

Conference Proceedings SIGGRAPH 2026 Conference Papers · July 19, 2026 Point tracking aims to follow visual points through complex motion, occlusion, and viewpoint changes, and has advanced rapidly with modern foundation models. Yet progress toward general point tracking remains constrained by limited high-quality data, as ex ... Full text Cite

DvD: Unleashing a Generative Paradigm for Document Dewarping via Coordinates-based Diffusion Model

Conference Proceedings SIGGRAPH Asia 2025 Conference Papers SA 2025 · December 14, 2025 Document dewarping aims to rectify deformations in photographic document images, thus improving text readability, which has attracted much attention and made great progress, but it is still challenging to preserve document structures. Given recent advances ... Full text Cite

Towards Training-Free Open-World Classification with 3D Generative Models

Conference Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 · October 27, 2025 3D open-world classification is a challenging yet essential task in dynamic and unstructured real-world scenarios, requiring robust subsequent knowledge adaptation capabilities. While current approaches predominantly rely on 2D pre-trained models through 3 ... Full text Cite

KDTalker++: Controllable Talking Portrait Generation with Audio, Text, and Expression Editing

Conference Mm 2025 Proceedings of the 33rd ACM International Conference on Multimedia Co Located with mm 2025 · October 27, 2025 This work presents KDTalker++, a real-time system for generating talking portrait videos from a single image using audio or text input. Built on a keypoint-based spatiotemporal diffusion model, it adds voice cloning, background editing, and fine-grained ex ... Full text Cite

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

Conference Mm 2024 Proceedings of the 32nd ACM International Conference on Multimedia · October 28, 2024 Photographed documents are prevalent but often suffer from deformations like curves or folds, hindering readability. Consequently, document dewarping has been widely studied, however its performance is still not satisfied due to lack of real training sampl ... 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

A Symbolic Characters Aware Model for Solving Geometry Problems

Conference Mm 2023 Proceedings of the 31st ACM International Conference on Multimedia · October 27, 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

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

Outpainting by Queries

Conference Lecture Notes in Computer Science · 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

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

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

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 · 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Preface

Conference Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics · January 1, 2014 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

Feature transformation with class conditional decorrelation

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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

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 · January 1, 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

Preface

Conference Proceedings IEEE International Conference on Data Mining Icdm · December 1, 2011 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

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

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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

Low rank metric learning with manifold regularization

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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

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 · January 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 · January 1, 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

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

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

GSML: A unified framework for sparse metric learning

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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

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

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

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

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

Direct zero-norm optimization for feature selection

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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 learning from general unlabeled data

Conference Proceedings IEEE International Conference on Data Mining Icdm · January 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

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

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

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 ... 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

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

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