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Rule Extraction from Support Vector Machines
Joachim . Ed(S): Diederich
€ 194.05
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Description for Rule Extraction from Support Vector Machines
Paperback. Editor(s): Diederich, Joachim. Series: Studies in Computational Intelligence. Num Pages: 274 pages, 55 black & white illustrations, 51 black & white tables, biography. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 14. Weight in Grams: 427.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2010
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
274
Condition
New
Series
Studies in Computational Intelligence
Number of Pages
262
Place of Publication
Berlin, Germany
ISBN
9783642094637
SKU
V9783642094637
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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