Radial Basis Function Network Pruning by Sensitivity Analysis

Daming Shi, Junbin Gao, Daniel So Yeung, Fei Chen

    Research output: Book chapter/Published conference paperConference paperpeer-review

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    Abstract

    Radial basis function (RBF) neural networks have been extensively used for classification and regression due to the fact that they can provide fast linear algorithms to approximate any regular function. The most critical issue in the construction of an RBF network for a given task is to determine the total number of radial basis functions, their centers and widths. Conventional methods of training an RBF network are to specify the radial basis function centers by searching for the optimal cluster centers of the training examples. This paper proposes a novel learning algorithm for construction of radial basis function by sensitive vectors (SenV), to which the output is the most sensitive. Our experiments are conducted on four benchmark datasets, and the results show that our proposed SenV-RBF classifier outperforms conventional RBFs and achieves the same level of accuracy as support vector machine.
    Original languageEnglish
    Title of host publicationAdvances in artificial intelligence, Canadian AI 2004, 17th conference of the Canadian Society for Computational Studies of Intelligence
    Place of PublicationBerlin/London
    PublisherSpringer
    Pages380-390
    Number of pages11
    Volume3060/2004
    DOIs
    Publication statusPublished - 2004
    EventConference of the Canadian Society for Computational Studies of Intelligence - London, Ontario, Canada, Canada
    Duration: 17 May 200419 May 2004

    Conference

    ConferenceConference of the Canadian Society for Computational Studies of Intelligence
    Country/TerritoryCanada
    Period17/05/0419/05/04

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