Conference Publication Details
Mandatory Fields
Howley, T,Madden, MG
ARTIFICIAL INTELLIGENCE REVIEW
The genetic kernel support vector machine: Description and evaluation
2005
November
Published
1
()
Optional Fields
classification genetic Kernel SVM genetic programming Mercer Kernel model selection support vector machine
379
395
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.
DOI 10.1007/s10462-005-9009-3
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