I thought readers of the Uncertainty in AI List might be interested in this
book. For more information please visit http://mitpress.mit.edu/0262194759
Learning with Kernels
Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Schölkopf and Alexander J. Smola
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 Biological
Cybernetics in Tübingen and a Researcher at Biowulf Technologies in New
York City. Alexander J. Smola is Leader of the Machine Learning Group,
Research School for Information Sciences and Engineering, the Australian
National University.
8 x 10, 632 pp., 138 illus., cloth, ISBN 0-262-19475-9
Adaptive Computation and Machine Learning series
Jud Wolfskill
Associate Publicist
MIT Press
5 Cambridge Center, 4th Floor
Cambridge, MA 02142
617.253.2079
617.253.1709 fax
wolfskil@mit.edu
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