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Format: Inbunden (Hardback)
Språk: Engelska
Antal sidor: 648
Utgivningsdatum: 2001-12-01
Upplaga: illustrated ed
Förlag: MIT Press
Medarbetare: Bach, Francis (series ed.)
Illustrationer: 138 illus.; 138 Illustrations, unspecified
Dimensioner: 260 x 210 x 35 mm
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Antal komponenter: 1
ISBN: 9780262194754

Learning with Kernels

Name: Learning with Kernels
Price: 885.00 SEK
Availability: OutOfStock
Author: Bernhard Schoelkopf, Alexander J Smola
ISBN: 9780262194754

Support Vector Machines, Regularization, Optimization, and Beyond

av Bernhard Schoelkopf, Alexander J Smola

Inbunden, Engelska, 2001-12-01

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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.

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"Interesting and original. Learning with Kernels will make a fine textbook on this subject."--Grace Wahba, Bascom Professor of Statistics, University of Wisconsin Madison "This splendid book fills the need for a comprehensive treatment of kernel methods and support vector machines. It collects results, theorems, and discussions from disparate sources into one very accessible exposition. I am particularly impressed that the authors have included problem sets at the end of each chapter; such problems are not easy to construct, but add significantly to the value of the book for the student audience."--Chris J. C. Burges, Microsoft Research

Bernhard Schoelkopf is Director at the Max Planck Institute for Intelligent Systems in Tubingen, 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.