Course Archives Statistical Quality Control & Operations Research Unit | |||
Course:Support Vector Machines Level: Postgraduate Time: Currently not offered |
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Syllabus Past Exams Syllabus: Learning machines: attraction and drawbacks of learning, generalization. SVM--History and Overview. Loss functions and their risks SVMs for classification: Hard margin SVMs--- linear SV classifiers for linearly separable data (maximal margin classifiers), Soft margin SVMs—Kernels and reproducing Kernel Hilbert spaces, construction of nonlinear SV classifiers, Linear and Non-linear SVCs in Matlab in Matlab, R (20 hours) Training of SVMs Infinite-sample versions of SVMs Statistical analysis of SVMs SVM for Regression-Linear and nonlinear SVRs Live cases with application of SVMs Reference Texts: 1. Statistical learning Theory: By V.N.Vapnik, John Wiley, New York, 1998. 2. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods: By Nello Cristianini and John Shawe-Taylor, Cambridge University Press, 2000. 3. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond: By B. Sch¨olkopf and A. J. Smola, MIT Press, Cambridge, MA, 2002. 4. Support Vector Machines: Theory and Applications: By Lipo Wang (Ed.), Springer- Verlag, New York, 2005. 5. Support Vector Machines for Pattern Classification: By Shigeo Abe, Springer-Verlag, London, 2005. Top of the page Past Exams | |||
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[Indian Statistical Institute] |