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

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

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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.

"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

Series Foreword
An Tutorial Introductionp. 1
Concepts and Toolsp. 23
Kernelsp. 25
Risk and Loss Functionsp. 61
Regularizationp. 87
Elements of Statistical Learning Theoryp. 125
Optimizationp. 149
Support Vector Machinesp. 187
Pattern Recognitionp. 189
Single-Class Problems: Qantile Estimation and Novelty Detectionp. 227
Regression Estimationp. 251
Implementationp. 279
Incorporating Invariancesp. 333
Learning Theory Revisitedp. 359
Kernel Methodsp. 405
Designing Kernelsp. 407
Kernel Feature Extractionp. 427
Kernel Fisher Discriminantp. 457
Bayesian Kernel Methodsp. 469
Regularized Principal Manifoldsp. 517
Pre-Images and Reduced Set Methodsp. 543
A: Addendap. 569
Mathematical Prerequisitesp. 575
Referencesp. 591
Indexp. 617
Notation and Symbolsp. 625
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780262194754
ISBN-10: 0262194759
Series: Adaptive Computation and Machine Learning Series
Audience: Professional
For Ages: 18+ years old
Format: Hardcover
Language: English
Number Of Pages: 648
Published: 7th December 2001
Country of Publication: US
Dimensions (cm): 26.3 x 21.0  x 3.5
Weight (kg): 1.5