Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond is an excellent illustration of this convergence of ideas from many fields. The development of kernel-based learning methods has resulted from a combination of machine learning theory, optimization algorithms from operations research, and kernel techniques from mathematical analysis. These three ideas have spread far beyond the original support-vector machine algorithm: Virtually every learning algorithm has been redesigned to exploit the power of kernel methods. Bernhard Schölkopf and Alexander Smola have written a comprehensive, yet accessible, account of these developments. This volume includes all of the mathematical and algorithmic background needed not only to obtain a basic understanding of the material but to master it. Students and researchers who study this book will be able to apply kernel methods in creative ways to solve a wide range of problems in science and engineering.
Thomas Dietterich