A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data

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

@InProceedings{conf/synasc/GirdeaC07,
  author =       "Marta Girdea and Liviu Ciortuz",
  title =        "A Hybrid Genetic Programming and Boosting Technique
                 for Learning Kernel Functions from Training Data",
  booktitle =    "Proceedings of the Ninth International Symposium on
                 Symbolic and Numeric Algorithms for Scientific
                 Computing, SYNASC 2007",
  year =         "2007",
  editor =       "Viorel Negru and Tudor Jebelean and Dana Petcu and 
                 Daniela Zaharie",
  pages =        "395--402",
  address =      "Timisoara, Romania",
  month =        sep # " 26-29",
  publisher =    "IEEE Computer Society",
  keywords =     "genetic algorithms, genetic programming, InfoBoost
                 procedure, RBF kernel function learning, boosting
                 technique, nonlinear SVM classification, training data,
                 learning (artificial intelligence), pattern
                 classification, radial basis function networks, support
                 vector machines",
  isbn13 =       "978-0-7695-3078-9",
  DOI =          "doi:10.1109/SYNASC.2007.71",
  size =         "8 pages",
  abstract =     "This paper proposes a technique for learning kernel
                 functions that can be used in non-linear SVM
                 classification. The technique uses genetic programming
                 to evolve kernel functions as additive or
                 multiplicative combinations of linear, polynomial and
                 RBF kernels, while a procedure inspired from InfoBoost
                 helps the evolved kernels concentrate on the most
                 difficult objects to classify. The kernels obtained at
                 each boosting round participate in the training of
                 non-linear SVMs which are combined, along with their
                 confidence coefficients, into a final classifier. We
                 compared on several data sets the performance of the
                 kernels obtained in this manner with the performance of
                 classic RBF kernels and of kernels evolved using a pure
                 GP method, and we concluded that the boosted GP kernels
                 are generally better.",
  notes =        "'Alexandru loan Cuza' Univ. of Iasi, Iasi",
  bibdate =      "2008-11-28",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#GirdeaC07",
}

Genetic Programming entries for Marta Girdea Liviu Ciortuz

Citations