Learning with support vector machines
By: Colin Campbell, Yiming Ying
Contributors: Ying, Yiming
Series: Synthesis lectures on artificial intelligence and machine learning -- # 10
Publisher: Morgan & Claypool
Published: 2011
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
ISBN-13: 9781608456178; 9781608456161
Subjects: Support vector machines
Other titles: Synthesis digital library of engineering and computer science.
Description: Support Vectors Machines have become a well established tool within machine learning.They work well in practice and have now been used across a wide range of applications from recognizing handwritten digits, to face identification, text categorisation, bioinformatics and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise.We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 75-82).

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.
Table of contents: title: Preface; title: Acknowledgments; title: 1. Support Vector Machines for classification; title: Introduction; title: Support Vector Machines for binary classification; title: Multi-class classification; title: Learning with noise: soft margins; title: Algorithmic implementation of Support Vector Machines; title: Case Study 1: training a Support Vector Machine; title: Case Study 2: predicting disease progression; title: Case Study 3: drug discovery through active learning; title: 2. Kernel-based models; title: Introduction; title: Other kernel-based learning machines; title: Introducing a confidence measure; title: One class classification; title: Regression: learning with real-valued labels; title: Structured output learning; title: 3. Learning with kernels; title: Introduction; title: Properties of kernels; title: Simple kernels; title: Kernels for strings and sequences; title: Kernels for graphs; title: Multiple kernel learning; title: Learning kernel combinations via a maximum margin approach; title: Algorithmic approaches to multiple kernel learning; title: Case study 4: protein fold prediction; title: A. Appendix; title: A.1. Introduction to optimization theory; title: A.2. Duality; title: A.3. Constrained optimization; title: Bibliography; title: Authors' biography.