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

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Bernhard Schölkopf (Director of the Max Planck Institute for Intelligent in Tübingen, Germany, Professor for Machine Lea, Max Planck Institute for Intelligent Systems) Alexander J. Smola

Learning with Kernels

Support Vector Machines, Regularization, Optimization, and Beyond

Bernhard Schölkopf (Director of the Max Planck Institute for Intelligent in Tübingen, Germany, Professor for Machine Lea, Max Planck Institute for Intelligent Systems), Alexander J. Smola, Francis Bach (INRIA - Willow Project-Team)

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English

MIT Press
05 June 2018

Computer science

Series: Adaptive Computation and Machine Learning series

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 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.

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 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.

By: Bernhard Schölkopf (Director of the Max Planck Institute for Intelligent in Tübingen Germany Professor for Machine Lea Max Planck Institute for Intelligent Systems), Alexander J. Smola
Series edited by: Francis Bach (INRIA - Willow Project-Team)
Imprint: MIT Press
Country of Publication: United States
Dimensions: Height: 254mm, Width: 203mm, Spine: 27mm
ISBN: 9780262536578
ISBN 10: 0262536579
Series: Adaptive Computation and Machine Learning series
Pages: 648
Publication Date: 05 June 2018
Recommended Age: From 18 years
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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 Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.