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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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