Training genetic programming classifiers by vicinal-risk minimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Ni:2014:GPEM,
  author =       "Ji Ni and Peter Rockett",
  title =        "Training genetic programming classifiers by
                 vicinal-risk minimization",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "1",
  pages =        "3--25",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Vicinal-risk minimisation",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9222-4",
  size =         "23 pages",
  abstract =     "We propose and motivate the use of vicinity-risk
                 minimisation (VRM) for training genetic programming
                 classifiers. We demonstrate that VRM has a number of
                 attractive properties and demonstrate that it has a
                 better correlation with generalisation error compared
                 to empirical risk minimisation (ERM) so is more likely
                 to lead to better generalisation performance, in
                 general. From the results of statistical tests over a
                 range of real and synthetic datasets, we further
                 demonstrate that VRM yields consistently superior
                 generalisation errors compared to conventional ERM.",
  notes =        "Department of Electronic and Electrical Engineering,
                 University of Sheffield, Mappin Street, Sheffield, S1
                 3JD, UK",
}

Genetic Programming entries for Ji Ni Peter I Rockett

Citations