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 Sch�lkopf 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 Sch�lkopf is Director at the Max Planck Institute for Biological Cybernetics in T�bingen 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 [EMAIL PROTECTED]
