Evolving kernels for support vector machine classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Keith M. Sullivan and Sean Luke",
  title =        "Evolving kernels for support vector machine
  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 =        "1702--1707",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1702.pdf",
  DOI =          "doi:10.1145/1276958.1277292",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Support
                 Vector Machines, SVM, STGP, ECJ",
  size =         "6 pages",
  abstract =     "While support vector machines (SVMs) have shown great
                 promise in supervised classification problems,
                 researchers have had to rely on expert domain knowledge
                 when choosing the SVM's kernel function. This project
                 seeks to replace this expert with a genetic programming
                 (GP) system. Using strongly typed genetic programming
                 and principled kernel closure properties, we introduce
                 a new algorithm, called KGP, which finds near-optimal
                 kernels. The algorithm shows wide applicability, but
                 the combined computational overhead of GP and SVMs
                 remains a major unresolved issue.",
  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

                 Cites \cite{DBLP:journals/air/HowleyM05}. Guarantees
                 closure and evolved kernel is valid (ie it will be a
                 Mercer Kernel). Positive definite gram matrix => Mercer

                 C-SVC, u-SVC

                 LIBSVM Mentions total noise datasets (ie Pima, page
                 1705) but does not give results. KGP. Not obvious why
                 evolved kernels work. Small difference between GP SVM
                 (KGP) and SVM-Grid?",

Genetic Programming entries for Keith M Sullivan Sean Luke