Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Gagne:PPSN:2006,
  author =       "Christian Gagne and Marc Schoenauer and 
                 Michele Sebag and Marco Tomassini",
  title =        "Genetic Programming for Kernel-Based Learning with
                 Co-evolving Subsets Selection",
  booktitle =    "Parallel Problem Solving from Nature - PPSN IX",
  year =         "2006",
  editor =       "Thomas Philip Runarsson and Hans-Georg Beyer and 
                 Edmund Burke and Juan J. Merelo-Guervos and 
                 L. Darrell Whitley and Xin Yao",
  volume =       "4193",
  pages =        "1008--1017",
  series =       "LNCS",
  address =      "Reykjavik, Iceland",
  publisher_address = "Berlin",
  month =        "9-13 " # sep,
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-38990-3",
  keywords =     "genetic algorithms, genetic programming,
                 hyperheuristic, DSS, coevolution, open beagle",
  URL =          "http://ppsn2006.raunvis.hi.is/proceedings/287.pdf",
  URL =          "http://arxiv.org/abs/cs/0611135",
  doi =          "doi:10.1007/11844297_102",
  size =         "10 pages",
  abstract =     "Support Vector Machines (SVMs) are well-established
                 Machine Learning (ML) algorithms. They rely on the fact
                 that i) linear learning can be formalised as a
                 well-posed optimisation problem; ii) nonlinear learning
                 can be brought into linear learning thanks to the
                 kernel trick and the mapping of the initial search
                 space onto a high dimensional feature space. The kernel
                 is designed by the ML expert and it governs the
                 efficiency of the SVM approach. In this paper, a new
                 approach for the automatic design of kernels by Genetic
                 Programming, called the Evolutionary Kernel Machine
                 (EKM), is presented. EKM combines a well-founded
                 fitness function inspired from the margin criterion,
                 and a co-evolution framework ensuring the computational
                 scalability of the approach. Empirical validation on
                 standard ML benchmark demonstrates that EKM is
                 competitive using state-of-the-art SVMs with tuned
                 hyper-parameters.",
  notes =        "PPSN-IX

                 evolved Kernels are forced to be symmetric functions.
                 Mercer's condition not enforced, but evolved. 3
                 co-evolving populations. runtime < 1 hour. Size based
                 parsimony pressure. Comparison with k-nn nearest
                 neighbours and SVM, GK-SVM (both with somewhat
                 optimised parameters). 6 undemanding UCI benchmarks.",
}

Genetic Programming entries for Christian Gagne Marc Schoenauer Michele Sebag Marco Tomassini