GPES: An Algorithm for Evolving Hybrid Kernel Functions of Support Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Phienthrakul:2007:cec,
  author =       "Tanasanee Phienthrakul and Boonserm Kijsirikul",
  title =        "GPES: An Algorithm for Evolving Hybrid Kernel
                 Functions of Support Vector Machines",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "2636--2643",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1717.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424803",
  abstract =     "The Support Vector Machine (SVM) is a popular approach
                 to the classification of data. One problem of SVM is
                 how to choose a kernel and the parameters for the
                 kernel. This paper proposes a classification technique,
                 called GPES, that combines Genetic Programming (GP) and
                 Evolutionary Strategies (ES) to evolve a hybrid kernel
                 for an SVM classifier. The hybrid kernels are
                 represented as trees that have some adjustable
                 parameters. These hybrid kernels are also the Mercer's
                 kernels. The experimental results are compared with a
                 standard SVM classifier using the polynomial and radial
                 basis function kernels with various parameter
                 settings.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Tanasanee Phienthrakul Boonserm Kijsirikul

Citations