New support vector machine formulations and algorithms with application to biomedical data analysis

New support vector machine formulations and algorithms with application to biomedical data analysis

The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript i](1-y[subscript i] f(x[subscript i]))₊ plus the L₂-norm of the weight vec...

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Bibliographic Details
Main Author: Guan, Wei
Published: Georgia Institute of Technology 2011
Subjects:
Ovarian cancer detection
Functional SVM
Biomarker discovery
Mixed-integer SVM
Fractional-norm SVM
Non-negative SVM
Ranking SVM
Protein folding energy function
Support vector machine optimization
Support vector machines
Algorithms
Bioinformatics
Machine learning
Online Access:http://hdl.handle.net/1853/41126

Internet

http://hdl.handle.net/1853/41126

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