Rule extraction from support vector machines
"Support vector machines (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 rea...
| Other Authors: | Diederich, Joachim., SpringerLink (Online service) |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin :
Springer,
©2008.
Berlin : [2008] |
| Physical Description: |
1 online resource (xii, 262 pages) : illustrations. |
| Series: |
Studies in computational intelligence ;
v. 80. |
| Subjects: |
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| 245 | 0 | 0 | |a Rule extraction from support vector machines / |c Joachim Diederich (ed.). |
| 260 | |a Berlin : |b Springer, |c ©2008. | ||
| 264 | 1 | |a Berlin : |b Springer, |c [2008] | |
| 264 | 4 | |c ©2008. | |
| 300 | |a 1 online resource (xii, 262 pages) : |b illustrations. | ||
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| 490 | 1 | |a Studies in computational intelligence, |x 1860-949X ; |v v. 80. | |
| 504 | |a Includes bibliographical references and indexes. | ||
| 520 | 1 | |a "Support vector machines (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." "This book provides an overview of the field and introduces a number of approaches to extracting rules from support vector machines developed by researchers. Successful applications are outlined and future research opportunities are discussed. This book will be a reference for researchers, graduate students, data mining practitioners, and data analysts."--Jacket. | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Rule Extraction from Support Vector Machines: An Introduction -- Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring -- Algorithms and Techniques -- Rule Extraction for Transfer Learning -- Rule Extraction from Linear Support Vector Machines via Mathematical Programming -- Rule Extraction Based on Support and Prototype Vectors -- SVMT-Rule: Association Rule Mining Over SVM Classification Trees -- Prototype Rules from SVM -- Applications -- Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines -- Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction -- Rule Extraction from SVM for Protein Structure Prediction. | |
| 546 | |a English. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Ingénierie. |2 eclas. | |
| 650 | 7 | |a Machine learning. |2 fast. | |
| 700 | 1 | |a Diederich, Joachim. | |
| 710 | 2 | |a SpringerLink (Online service) | |
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