Knowledge Extraction From a Class of Support Vector Machines: A Fuzzy Logic Approach
نویسندگان
چکیده
Support vector machines (SVMs) proved to be highly efficient computational tools in various classification tasks. However, the knowledge learned by an SVM is encoded in a long list of parameter values, and it is not easy to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy–rule base, the fuzzy all–permutations rule base (FARB). The equivalent FARB provides a symbolic representation of the SVM functioning. This leads to a new approach for knowledge extraction from SVMs. Several examples demonstrate the effectiveness of this approach.
منابع مشابه
Knowledge Extraction from Support Vector Machines: A Fuzzy Logic Approach
Support vector machines (SVMs) proved to be highly efficient computational tools in various classification tasks. However, SVMs are nonlinear classifiers and the knowledge learned by an SVM is encoded in a long list of parameter values, making it difficult to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy–rule ...
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