A GP-based kernel construction and optimization method for RVM

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@InProceedings{Wu:2010:ICCAE,
  author =       "Bing Wu and Wen-qiong Zhang and Ling Chen and 
                 Jia-hong Liang",
  title =        "A GP-based kernel construction and optimization method
                 for RVM",
  booktitle =    "The 2nd International Conference on Computer and
                 Automation Engineering (ICCAE 2010)",
  year =         "2010",
  month =        "26-28 " # feb,
  volume =       "4",
  pages =        "419--423",
  abstract =     "Selecting a suitable kernel for relevance vector
                 machine is one of most challenging aspects of
                 successfully using this learning tool. Efficiently
                 automating the search for such a kernel is therefore
                 desirable. This paper proposes a data-driven kernel
                 function construction and optimisation method, which
                 combines genetic programming (GP) and relevance vector
                 regression to evolve an optimal or near-optimal kernel
                 function, named GP-Kernel. The evolved kernel is
                 compared to several widely used kernels on several
                 regression benchmark datasets. Empirical results
                 demonstrate that RVM using such GP-Kernel can
                 outperform or match the best performance of standard
                 kernels.",
  keywords =     "genetic algorithms, genetic programming, GP-based
                 kernel construction, RVM, SVM, data- driven kernel
                 function construction, optimisation method, relevance
                 vector machine, relevance vector regression, regression
                 analysis, support vector machines",
  DOI =          "doi:10.1109/ICCAE.2010.5451646",
  notes =        "Also known as \cite{5451646}",
}

Genetic Programming entries for Bing Wu Wen-Qiong Zhang Ling Chen Jia-Hong Liang

Citations