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The genetic kernel support vector machine: description and evaluation

Howley, Tom
; 
Madden, Michael G.
Identifiers
http://hdl.handle.net/10379/9320
 https://doi.org/10.13025/24190
Publication Date
2005-11-01
Keywords
classification, genetic kernel svm, genetic programming, mercer kernel, model selection, support vector machine
Type
Article
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Citation
Howley, Tom; Madden, Michael G. (2005). The genetic kernel support vector machine: description and evaluation. Artificial Intelligence Review 24 (3), 379-395
Abstract
The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Genetic Kernel SVM (GK SVM), that uses Genetic Programming to evolve a kernel for a SVM classifier. Results of initial experiments with the proposed technique are presented. These results are compared with those of a standard SVM classifier using the Polynomial, RBF and Sigmoid kernel with various parameter settings.
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Publisher
Springer Nature
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Externally hosted open access publications with University of Galway authors (1)