%0 Journal Article %1 DBLP:journals/air/HowleyM05 %A Howley, Tom %A Madden, Michael G. %D 2005 %J Artificial Intelligence Review %K Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector %N 3-4 %P 379--395 %R doi:10.1007/s10462-005-9009-3 %T The Genetic Kernel Support Vector Machine: Description and Evaluation %V 24 %X 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

@article{DBLP:journals/air/HowleyM05, 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}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Howley, Tom and Madden, Michael G.}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/2274c31a292dbfd212858667cfc3a5a7f/brazovayeye}, doi = {doi:10.1007/s10462-005-9009-3}, interhash = {049d119c464b49ce8ed886384c9d79e1}, intrahash = {274c31a292dbfd212858667cfc3a5a7f}, issn = {0269-2821}, journal = {Artificial Intelligence Review}, keywords = {Kernel Kernel, Mercer SVM, algorithms, classification, genetic machine model programming, selection, support vector}, number = {3-4}, pages = {379--395}, timestamp = {2008-06-19T17:41:52.000+0200}, title = {The Genetic Kernel Support Vector Machine: Description and Evaluation}, volume = 24, year = 2005 }