Titlebook: Rule Extraction from Support Vector Machines; Joachim Diederich (Honorary Professor) Book 2008 Springer-Verlag Berlin Heidelberg 2008 Supp

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書目名稱Rule Extraction from Support Vector Machines讀者反饋學(xué)科排名

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SVMT-Rule: Association Rule Mining Over SVM Classification Treesmpared to SVM-Rule, decision-tree is a simple, but very efficient rule extraction method in terms of comprehensibility [33]. The obtained rules from decision tree may not be so accurate as SVM rules, but they are easy to comprehend because that every rule represents one decision path that is traceable in the decision tree.

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BRIBE

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Canyon

Rule Extraction from Linear Support Vector Machines via Mathematical Programmingonoverlapping rules that, unlike the original classifier, can be easily interpreted by humans..Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve. We discuss various properties of the algorithm and provide

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Rule Extraction Based on Support and Prototype Vectorsor 2000; Vapnik 1998), which has been successfully applied initially in classification problems and later extended in different domains to other kind of problems like regression or novel detection. As a learning tool, it has demonstrated its strength especially in the cases where a data set of reduc

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Prototype Rules from SVMedge representation. In P-rules knowledge is represented as set of reference vectors, that may be derived from the SVM model..The number of support vectors (SV) should be reduced to a minimal number that still preserves SVM generalization abilities. Several state-of-the-art methods that reduce the n