Extracting the knowledge embedded in support vector machines

By Xiuju FU, ChongJin Ong, S. Sathiya Keerthi, GihGuang Hung, Liping Goh. 2004

73 citations recorded by the source corpus.

How researchers see this paper

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Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, positive): “The second class of simpliﬁ-cations can be exempliﬁed by [98] , which proposed the addition of the SVM’s hyperplane, along with the support vectors, to the components in charge of creating the rules.”
Fuzzy rule extraction from support vector machines (2005, methodology): “RulExtSVM algorithm is divided into three steps.”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “proposes a rule-extraction approach (RuleExSVM) to obtain hyper-rectangular rules based on the information provided by support vectors and its decision function [43].”
Rule extraction from support vector machines: A review (2010, methodology): “[30] suggested a method (RulExSVM) for rule extraction from non-linear SVMs, trained with a radial basis function (RBF) kernel utilizing the SVM decision boundary in addition to the SVs.”
Rule Extraction from Support Vector Machines: A Sequential Covering Approach (2007, methodology): “Therefore, several methods have been introduced for rule extraction from both ANNs [2] and, more recently, SVMs [1], [3], [4], [5], [6], [7], [8], which will be summarized in Section 4.”