Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond - Paperback
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Availability:In StockContributor:Bernhard Scholkopf, Alexander J. SmolaSeries:Adaptive Computation and Machine LearningAudience:Young AdultPublish date:2018-06-05Pages:648
Language:EnglishPublisher:MIT PressISBN-13:9780262536578ISBN-10:262536579UPC:9780262536578Book Category:Computers, MathematicsBook Subcategory:Computer ScienceSize:10.00 x 8.00 x 1.30 inchesWeight:2.7822Product ID:SCXZTDJJ5B
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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...
Series: Adaptive Computation and Machine Learning
Audience: Young Adult
Language:EnglishPublisher:MIT PressISBN-13:9780262536578ISBN-10:262536579UPC:9780262536578Book Category:Computers, MathematicsBook Subcategory:Computer ScienceSize:10.00 x 8.00 x 1.30 inchesWeight:2.7822Product ID:SCXZTDJJ5B
Bernhard Sch?lkopf is Director at the Max Planck Institute for Intelligent Systems 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...
Publisher: MIT Press
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