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

ISBN-10: 0262194759

ISBN-13: 9780262194754

Edition: 2002

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Description: 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 Kernelsprovides 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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Book details

Copyright year: 2002
Publisher: MIT Press
Publication date: 12/7/2001
Binding: Hardcover
Pages: 648
Size: 8.25" wide x 10.25" long x 1.25" tall
Weight: 3.520
Language: English

Bernhard Sch�lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in T�bingen, 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.

Series Foreword
Preface
An Tutorial Introduction
Concepts and Tools
Kernels
Risk and Loss Functions
Regularization
Elements of Statistical Learning Theory
Optimization
Support Vector Machines
Pattern Recognition
Single-Class Problems: Qantile Estimation and Novelty Detection
Regression Estimation
Implementation
Incorporating Invariances
Learning Theory Revisited
Kernel Methods
Designing Kernels
Kernel Feature Extraction
Kernel Fisher Discriminant
Bayesian Kernel Methods
Regularized Principal Manifolds
Pre-Images and Reduced Set Methods
A: Addenda
Mathematical Prerequisites
References
Index
Notation and Symbols
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