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

Name: Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond
Price: 47.87 USD
Availability: InStock
ISBN: 9780262194754

ISBN-10: 0262194759

ISBN-13: 9780262194754

Edition: 2001

Authors: Bernhard Schï¿½lkopf, Alexander J. Smola, Francis Bach

List price: $95.00

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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…

Book details

List price: $95.00
Copyright year: 2001
Publisher: MIT Press
Publication date: 12/7/2001
Binding: Hardcover
Pages: 644
Size: 8.25" wide x 10.25" long x 1.25" tall
Weight: 3.542
Language: English

Bernhard Schï¿½lkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tï¿½bingen, 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