Rule Extraction from Support Vector Machines
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 comprehe...
Uloženo v:
| Hlavní autor: | |
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2008. |
| Edice: | Studies in Computational Intelligence
80 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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| Edition sous un autre format: | • Rule Extraction from Support Vector Machines, Texte imprimé, 9783642094637 • Rule Extraction from Support Vector Machines, Texte imprimé, 9783540844518 • Rule Extraction from Support Vector Machines, Texte imprimé, 9783540753896 • Rule Extraction from Support Vector Machines, Texte imprimé, 9783642094637 • Rule Extraction from Support Vector Machines, Texte imprimé, 9783540844518 • Rule Extraction from Support Vector Machines, Texte imprimé, 9783540753896 |
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| 100 | 1 | |a Diederich, Joachim. | |
| 245 | 1 | 0 | |a Rule Extraction from Support Vector Machines |c edited by Joachim Diederich. |
| 250 | |a 1st ed. 2008. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 80 |x 1860-9503 | |
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| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. | ||
| 700 | 1 | |a Diederich, Joachim. |4 edt | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783642094637 | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783540844518 | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783540753896 | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783642094637 | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783540844518 | |
| 776 | 0 | |t Rule Extraction from Support Vector Machines |b Texte imprimé |z 9783540753896 | |
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