Empirical estimation of functional relationships between Q value of the L-GEM and training data using genetic programming

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@InProceedings{Huang:2012:ICML,
  author =       "Zhi-Qian Huang and Wing W. Y. Ng",
  booktitle =    "Proceedings of the 2012 International Conference on
                 Machine Learning and Cybernetics, ICML 2012",
  title =        "Empirical estimation of functional relationships
                 between {Q} value of the {L-GEM} and training data
                 using genetic programming",
  year =         "2012",
  volume =       "1",
  pages =        "341--348",
  month =        "15-17 " # jul,
  address =      "Xian",
  size =         "8 pages",
  abstract =     "The Localised Generalisation Error Model (L-GEM)
                 provides a practical framework for evaluating
                 generalisation capability of a learning machine , e.g.
                 neural network. The Q value of the L-GEM controls the
                 coverage of unseen samples under evaluation. Owing to
                 the nonlinear and real unknown relationship of unseen
                 samples and their generalisation error, different Q
                 values yield different L-GEM values. In this paper, we
                 adopt an evolutionary procedure based on genetic
                 programming and artificial datasets to estimate
                 functional relationship between Q values and statistics
                 of training samples. In this first empirical study, a
                 simple training samples generated from two
                 two-dimensional Gaussian distribution is adopted.
                 Resulting formulae provide hints to select optimal Q
                 value for given classification problems.",
  keywords =     "genetic algorithms, genetic programming, Gaussian
                 distribution, generalisation (artificial intelligence),
                 learning (artificial intelligence), pattern
                 classification, 2D Gaussian distribution, L-GEM, Q
                 value, artificial dataset, classification problems,
                 empirical estimation, evolutionary procedure,
                 functional relationship, generalisation error,
                 localized generalisation error model, machine learning,
                 statistics, training data sample, Abstracts,
                 Programming, Localised Generalisation Error Model,
                 Q-neighbourhood",
  DOI =          "doi:10.1109/ICMLC.2012.6358937",
  ISSN =         "2160-133X",
  notes =        "Also known as \cite{6358937}",
}

Genetic Programming entries for Zhi-Qian Huang Wing W Y Ng

Citations