- Introduces a number of different approaches to extracting rules from support vector machines developed by key researchers in the field
- Successful applications are outlined and future research opportunities are discussed
Buy this book
- About this book
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Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made.
This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.
- Table of contents (10 chapters)
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Rule Extraction from Support Vector Machines: An Introduction
Pages 3-31
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Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring
Pages 33-63
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Rule Extraction for Transfer Learning
Pages 67-82
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Rule Extraction from Linear Support Vector Machines via Mathematical Programming
Pages 83-107
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Rule Extraction Based on Support and Prototype Vectors
Pages 109-134
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Table of contents (10 chapters)
- Download Preface 1 PDF (42.8 KB)
- Download Sample pages 1 PDF (1.4 MB)
- Download Table of contents PDF (59.8 KB)
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- Rule Extraction from Support Vector Machines
- Editors
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- Joachim Diederich
- Series Title
- Studies in Computational Intelligence
- Series Volume
- 80
- Copyright
- 2008
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-540-75390-2
- DOI
- 10.1007/978-3-540-75390-2
- Hardcover ISBN
- 978-3-540-75389-6
- Softcover ISBN
- 978-3-642-09463-7
- Series ISSN
- 1860-949X
- Edition Number
- 1
- Number of Pages
- XII, 262
- Number of Illustrations
- 55 b/w illustrations
- Topics
