Learning with support vector machines
- Responsibility
- Colin Campbell, Yiming Ying.
- Imprint
- Cham, Switzerland : Springer, ©2011.
- Physical description
- 1 online resource (viii, 83 pages) : illustrations
- Series
- Synthesis digital library of engineering and computer science.
- Synthesis lectures on artificial intelligence and machine learning ; #10. 1939-4608
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Campbell, Colin.
- Contributor
- Ying, Yiming.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 75-82).
- Contents
-
- Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
- (source: Nielsen Book Data)
- Publisher's summary
-
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 hand-written 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.
(source: Nielsen Book Data)
Subjects
Bibliographic information
- Publication date
- 2011
- Series
- Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #10
- Note
- Series from website.
- Referenced in
- Compendex
- INSPEC
- Google scholar
- Google book search
- ISBN
- 9781608456178 (electronic bk.)
- 160845617X (electronic bk.)
- 1608456161
- 9781608456161 (pbk.)
- 9783031015526 (electronic bk.)
- 3031015525 (electronic bk.)
- DOI
- 10.2200/S00324ED1V01Y201102AIM010
- 10.1007/978-3-031-01552-6
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