The Genetic Kernel Support Vector Machine: Description and Evaluation

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  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,",
  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