The Genetic Kernel Support Vector Machine: Description and Evaluation

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@Article{DBLP:journals/air/HowleyM05,

• author = "Tom Howley and Michael G. Madden",
• title = "The Genetic Kernel Support Vector Machine: Description and Evaluation",
• journal = "Artificial Intelligence Review",
• volume = "24",
• number = "3-4",
• year = "2005",
• pages = "379--395",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• keywords = "genetic algorithms, genetic programming, classification, genetic Kernel SVM, Mercer Kernel, model selection, support vector machine",
• ISSN = "0269-2821",
• DOI = "doi:10.1007/s10462-005-9009-3",
• 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",

}

Genetic Programming entries for Tom Howley Michael G Madden

Citations