Support Vector Machine
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Subject Areas on Research
- A Supervised Approach to Robust Photoplethysmography Quality Assessment.
- A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.
- A prior feature SVM-MRF based method for mouse brain segmentation.
- 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.
- Decoding individual finger movements from one hand using human EEG signals.
- Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.
- Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation.
- 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.
- 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.
- Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study.
- 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