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

Bernhard Scholkopf (u. a.)

ISBN 10: 0262536579 / ISBN 13: 9780262536578

Published by MIT Press, 2018

About this Item

Learning with Kernels | Support Vector Machines, Regularization, Optimization, and Beyond | Bernhard Scholkopf (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2018 | MIT Press | EAN 9780262536578 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Seller Inventory # 113981731

Bibliographic Details

• Title: Learning with Kernels | Support Vector Machines, Regularization, Optimization, and Beyond
• Publisher: MIT Press
• Publication Date: 2018
• Language: English
• ISBN 10: 0262536579
• ISBN 13: 9780262536578
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 203H x 35D millimeters 254 millimeters width by 203 millimeters height by 35 millimeters depth
• Weight: 1363 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, 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.