Preliminary Study on Additive Radial Basis Function Networks

Shih-Hui Liao, Chin-Teng Lin, Jyh-Yeong Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a new class of learning models, namely the additive radial basis function networks (ARBFNs) for general nonlinear regression problems are proposed. This class of learning machines combines the radial basis function networks (RBFNs) commonly used in general machine learning problems and the additive models (AMs) frequently encountered in semi parametric regression problems. In statistical regression theory, AM is a good compromise between the linear parametric model and the non parametric model. Simulation results show that for the given learning problem, ARBFNs usually need fewer hidden nodes than those of RBFNs for the same level of accuracy.
Original languageAmerican English
Title of host publicationIEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC 2010)
PublisherIEEE
Pages3113-3117
Number of pages5
ISBN (Print)978-1-4244-6588-0
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010 - Istanbul, Turkey
Duration: 10 Oct 201013 Oct 2010

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN (Print)1062-922X

Conference

Conference2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010
CountryTurkey
CityIstanbul
Period10/10/1013/10/10

Keywords

  • additive radial basis function network (ARBFN); radial basis function network (RBFN); additive model (AM); semi parametric regression

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