SVM Book

SVM Books

• CHERKASSKY, Vladimir and Filip MULIER, Learning from Data: Concepts, Theory, and Methods
• CRISTIANINI, N. and J. SHAWE-TAYLOR, An Introduction to Support Vector Machines and other kernel-based learning methods
• DUDA, Richard O., Peter E. HART, David G. STORK, Pattern Classification
• HASTIE, Trevor, Robert TIBSHIRANI and Jerome FRIEDMAN, The Elements of Statistical Learning : Data Mining, Inference, and Prediction
• HERBRICH, Ralf, Learning Kernel Classifiers : Theory and Algorithms
• KECMAN, Vojislav, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
• RYCHETSKY, Matthias, Algorithms and Architectures for Machine Learning based on Regularized Neural Networks and Support Vector Approaches
• SCHÖLKOPF, Bernhard and Alex SMOLA, Learning with Kernels -- Support Vector Machines, Regularization, Optimization and Beyond
• SCHÖLKOPF, Bernhard, Christopher J. C. BURGES and Alexander J. SMOLA (edited by), Advances in Kernel Methods : Support Vector Learning
• SHAWE-YAYLOR and Nello CRISTIANINI, Kernel Methods for Pattern Analysis
• SMOLA, Alexander J., Peter J. BARTLETT, Bernhard SCHÖLKOPF and Dale SCHUURMANS (edited by), Advances in Large Margin Classifiers
• SUYKENS, Johan A. K., et al., Least Squares Support Vector Machines
• SUYKENS, Johan, et al., Advances in Learning Theory: Methods, Models and Applications
• VAPNIK, Vladimir N., The Nature of Statistical Learning Theory
• VAPNIK, Vladimir N., Statistical Learning Theory
• WANG, Lipo (Editor), 2005. Support Vector Machines: Theory and Applications, Springer.

SVM Book Reviews

• PEDRYCZ, Witold, Book reviews
• VLACIC, Ljubo, Book reviews

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Himanshu Rai

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