Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

Bernhard Schoelkopf, Alexander J. Smola

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    MIT Press

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Beschreibung

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.

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.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Produktdetails

Einband Taschenbuch
Seitenzahl 648
Erscheinungsdatum 05.06.2018
Sprache Englisch
ISBN 978-0-262-53657-8
Reihe Adaptive Computation and Machine Learning series
Verlag MIT Press
Maße (L/B/H) 25.4/20.4/3.2 cm
Gewicht 1273 g
Abbildungen 138 illus. 138 Illustrations, unspecified

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