Bernhard Schölkopf

Francis Bach

Alexander J. Smola

Director of the Max Planck Institute for Intelligent in Tübingen Germany Professor for Machine Lea Bernhard Schölkopf

rnhard Schölkopf

Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen Germany Profe Bernhard Scholkopf

This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.

Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels

Bernhard Scholkopf

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Alexander J. Smola

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