John Wiley & Sons Inc Huang, S: Statistical Learning

This text offers an overview of machine learning with an emphasis on its statistical aspects. The book contains an introduction to support vector machines (SVMs), and presents kernel-based learning algorithms, learning theory with reproducing kernel Hilbert space (RKHS), and coverage of neural networks. Bayesian theory is intertwined with the presented material when applicable. This book promotes a hands-on, accessible approach and each chapter contains numerous reproducible examples and exercises. A detailed appendix serves as a 'toolbox', with a range of topics of including: KGaussian, Smooth SVM, Lagrangian SVM, Uniform Designs for Model Selection, Support Vector Regression, Kernel PCA, Kernel SIR, Kernel Fisher Linear Discriminant Analysis, and Kernel CCA.