Genetically designed multiple-kernels for improving the SVM performance

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Laura Diosan and Mihai Oltean and 
                 Alexandrina Rogozan and Jean Pierre Pecuchet",
  title =        "Genetically designed multiple-kernels for improving
                 the SVM performance",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1873--1873",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277332",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming,
                 Genetics-Based Machine Learning: Poster, kernel,
                 Support Vector Machines, SVM",
  abstract =     "Classical kernel-based classifiers only use a single
                 kernel, but the real-world applications have emphasised
                 the need to consider a combination of kernels - also
                 known as a multiple kernel - in order to boost the
                 performance. Our purpose is to automatically find the
                 mathematical expression of a multiple kernel by
                 evolutionary means. In order to achieve this purpose we
                 propose a hybrid model that combines a Genetic
                 Programming (GP) algorithm and a kernel-based Support
                 Vector Machine (SVM) classifier. Each GP chromosome is
                 a tree encoding the mathematical expression of a
                 multiple kernel. Numerical experiments show that the
                 SVM embedding the evolved multiple kernel performs
                 better than the standard kernels for the considered
                 classification problems.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071

                 Evolved SVM Kernel forced to remain valid because only
                 legal combinations (ie plus, times, exp) of legal
                 kernels (linear, plynomial, RBF) is allowed.",

Genetic Programming entries for Laura Diosan Mihai Oltean Alexandrina Rogozan Jean Pierre Pecuchet