Support Vector Machine

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  Subject Areas on Research

  • A SVM-based quantitative fMRI method for resting-state functional network detection. 
  • A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits. 
  • A novel approach for prediction of vitamin d status using support vector regression. 
  • A prior feature SVM-MRF based method for mouse brain segmentation. 
  • A unified machine learning method for task-related and resting state fMRI data analysis. 
  • A unifying framework for interpreting and predicting mutualistic systems. 
  • Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease. 
  • Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms. 
  • Automatic diagnosis of pathological myopia from heterogeneous biomedical data. 
  • Big data: More than big data sets. 
  • Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections. 
  • Connectome-scale assessments of structural and functional connectivity in MCI. 
  • Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility. 
  • Decoding individual finger movements from one hand using human EEG signals. 
  • Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI. 
  • Enhancement of Risk Prediction With Machine Learning: Rise of the Machines. 
  • Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies. 
  • Human pluripotent stem cell-derived neural constructs for predicting neural toxicity. 
  • Identification of MCI individuals using structural and functional connectivity networks. 
  • Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data. 
  • Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. 
  • Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. 
  • Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine. 
  • SNP selection in genome-wide association studies via penalized support vector machine with MAX test. 
  • Single-Channel Real-Time Drowsiness Detection Based on Electroencephalography. 
  • Spatially regularized machine learning for task and resting-state fMRI. 
  • Stability selection for regression-based models of transcription factor-DNA binding specificity. 
  • Statistical methods in metabolomics. 
  • Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. 
  • Using channel-specific statistical models to detect reverberation in cochlear implant stimuli. 
  • WebGLORE: a web service for Grid LOgistic REgression. 
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  Keywords of People

  • Gordan, Raluca Mihaela, Associate Professor in Biostatistics & Bioinformatics, Computer Science
  • Hartemink, Alexander J., Professor in the Department of Computer Science, Biology