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

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

by Bernhard Sch?lkopf, Alexander J. Smola
     
 

ISBN-10: 0262194759

ISBN-13: 9780262194754

Pub. Date: 12/15/2001

Publisher: MIT Press

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

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Overview

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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Product Details

ISBN-13:
9780262194754
Publisher:
MIT Press
Publication date:
12/15/2001
Series:
Adaptive Computation and Machine Learning series
Edition description:
New Edition
Pages:
648
Product dimensions:
8.00(w) x 10.00(h) x 1.06(d)
Age Range:
18 Years

Table of Contents

Series Foreword
Preface
1An Tutorial Introduction1
IConcepts and Tools23
2Kernels25
3Risk and Loss Functions61
4Regularization87
5Elements of Statistical Learning Theory125
6Optimization149
IISupport Vector Machines187
7Pattern Recognition189
8Single-Class Problems: Qantile Estimation and Novelty Detection227
9Regression Estimation251
10Implementation279
11Incorporating Invariances333
12Learning Theory Revisited359
IIIKernel Methods405
13Designing Kernels407
14Kernel Feature Extraction427
15Kernel Fisher Discriminant457
16Bayesian Kernel Methods469
17Regularized Principal Manifolds517
18Pre-Images and Reduced Set Methods543
A: Addenda569
BMathematical Prerequisites575
References591
Index617
Notation and Symbols625

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