Radial Basis Function Network Pruning by Sensitivity Analysis

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

Research output: Book chapter/Published conference paperConference paper

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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
CountryCanada
Period17/05/0419/05/04

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    Shi, D., Gao, J., So Yeung, D., & Chen, F. (2004). Radial Basis Function Network Pruning by Sensitivity Analysis. In Advances in artificial intelligence, Canadian AI 2004, 17th conference of the Canadian Society for Computational Studies of Intelligence (Vol. 3060/2004, pp. 380-390). Springer. https://doi.org/10.1007/b97823