Genetic complex multiple kernel for relevance vector regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Bing Wu and Wen-Qiong Zhang and Zhi-Wei Hu and 
                 Jia-Hong Liang",
  title =        "Genetic complex multiple kernel for relevance vector
  booktitle =    "2nd International Conference on Advanced Computer
                 Control (ICACC 2010)",
  year =         "2010",
  month =        "27-29 " # mar,
  volume =       "4",
  pages =        "217--221",
  abstract =     "Relevance vector machine (RVM) is a state-of-the-art
                 technique for regression and classification, as a
                 sparse Bayesian extension version of the support vector
                 machine. The selection of a kernel and associated
                 parameter is a critical step of RVM application. The
                 real-world application and recent researches have
                 emphasised the requirement to multiple kernel learning,
                 in order to boost the fitting accuracy by adapting
                 better the characteristics of the data. This paper
                 presents a data-driven evolutionary approach, called
                 Genetic Complex Multiple Kernel Relevance Vector
                 Regression (GCMK RVR), which combines genetic
                 programming(GP) and relevance vector regression to
                 evolve an optimal or near-optimal complex multiple
                 kernel function. Each GP chromosome is a tree that
                 encodes the mathematical expression of a complex
                 multiple kernel function. Numerical experiments on
                 several benchmark datasets show that the RVR involving
                 this GCMK perform better than not only the widely used
                 simple kernel, Polynomial, Gaussian RBF and Sigmoid
                 kernel, but also the convex linear multiple kernel
  keywords =     "genetic algorithms, genetic programming,
                 classification method, data driven evolutionary
                 approach, genetic complex multiple kernel, genetic
                 complex multiple kernel relevance vector regression,
                 multiple kernel learning, relevance vector machine,
                 sparse Bayesian extension version, support vector
                 machine, Bayes methods, belief networks, learning
                 (artificial intelligence), numerical analysis, pattern
                 classification, regression analysis, support vector
  DOI =          "doi:10.1109/ICACC.2010.5486939",
  notes =        "Coll. of Mech. Eng. & Autom., Nat. Univ. of Defense
                 Technol., Changsha, China Also known as

Genetic Programming entries for Bing Wu Wen-Qiong Zhang Zhi-Wei Hu Jia-Hong Liang