Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Adaptive Computation and Machine Learning

by Bernhard Schölkopf, Alexander J. Smola

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 the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs-kernelsfor 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.

Genres: Artificial Intelligence, Computer Science, Science, Mathematics

644 pages, Hardcover

First published December 7, 2001

About the author

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Reviews

[पूजा · Rating 4 out of 5]

very good book for understanding concepts of Support Vector Machines.

[Michiel · Rating 3 out of 5]

Both practical and interesting. I thought I knew something about kernels, but apparently there was/still quite some more to them :)

[Gaurav Singh · Rating 5 out of 5]

Great Book for Kernel Theory.

[Cristian Ermurache · Rating 5 out of 5]

tentacule lingvistice ce se întind în lirism