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An efficient MDL-based construction of RBF networks

Authors:

Aleš Leonardis,

Horst BischofAuthors Info & Claims

Neural Networks, Volume 11, Issue 5

Pages 963 - 973

https://doi.org/10.1016/S0893-6080(98)00051-3

Published: 01 July 1998 Publication History

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Zhang YLi YSun JJi J(2015)Estimates on compressed neural networks regressionNeural Networks10.1016/j.neunet.2014.10.00863:C(10-17)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.neunet.2014.10.008
Zhou PLi DWu HCheng F(2011)The automatic model selection and variable kernel width for RBF neural networksNeurocomputing10.1016/j.neucom.2011.07.01174:17(3628-3637)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.neucom.2011.07.011
Tian JLi MChen F(2010)Dual-population based coevolutionary algorithm for designing RBFNN with feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.03.03137:10(6904-6918)Online publication date: 1-Oct-2010
https://dl.acm.org/doi/10.1016/j.eswa.2010.03.031
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An efficient MDL-based construction of RBF networks

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Information

Published In

Neural Networks Volume 11, Issue 5

July 1, 1998

184 pages

ISSN:0893-6080

Editors:
Stephen Grossberg
Boston Univ., Boston, MA
,
Mitsuo Kawato
ATR Human Information Processing Research Laboratories, Kyoto, Japan
,
John Taylor
Univ. of London, UK

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 July 1998

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Zhang YLi YSun JJi J(2015)Estimates on compressed neural networks regressionNeural Networks10.1016/j.neunet.2014.10.00863:C(10-17)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.neunet.2014.10.008
Zhou PLi DWu HCheng F(2011)The automatic model selection and variable kernel width for RBF neural networksNeurocomputing10.1016/j.neucom.2011.07.01174:17(3628-3637)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1016/j.neucom.2011.07.011
Tian JLi MChen F(2010)Dual-population based coevolutionary algorithm for designing RBFNN with feature selectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.03.03137:10(6904-6918)Online publication date: 1-Oct-2010
https://dl.acm.org/doi/10.1016/j.eswa.2010.03.031
Wang JLin HHsu YYang Y(2009)Using a Wiener-Type Recurrent Neural Network with the Minimum Description Length Principle for Dynamic System IdentificationProceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence10.1007/978-3-540-74205-0_22(192-201)Online publication date: 17-Nov-2009
https://dl.acm.org/doi/10.1007/978-3-540-74205-0_22
Li MTian JChen F(2008)Improving multiclass pattern recognition with a co-evolutionary RBFNNPattern Recognition Letters10.1016/j.patrec.2007.10.01929:4(392-406)Online publication date: 1-Mar-2008
https://dl.acm.org/doi/10.1016/j.patrec.2007.10.019
Rivas VArenas MMerelo JPrieto A(2007)EvRBFProceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 710.5555/1348011.1348029(98-103)Online publication date: 24-Aug-2007
https://dl.acm.org/doi/10.5555/1348011.1348029
Tian JLi MChen F(2007)An Evolutionary RBFNN Learning Algorithm for Complex Classzification ProblemsProceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks10.1007/978-3-540-72393-6_54(448-456)Online publication date: 3-Jun-2007
https://dl.acm.org/doi/10.1007/978-3-540-72393-6_54
Du HLam JZhang N(2006)Modelling of a magneto-rheological damper by evolving radial basis function networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2006.02.00519:8(869-881)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.1016/j.engappai.2006.02.005
Liu JLampinen JO'Reilly U(2005)A differential evolution based incremental training method for RBF networksProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068157(881-888)Online publication date: 25-Jun-2005
https://dl.acm.org/doi/10.1145/1068009.1068157
Valls JAler RFernández O(2005)Using a mahalanobis-like distance to train radial basis neural networksProceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems10.1007/11494669_32(257-263)Online publication date: 8-Jun-2005
https://dl.acm.org/doi/10.1007/11494669_32
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RBF neural network based on q-Gaussian function in function approximation

Function emulation using radial basis function networks

Stable calculation of Gaussian-based RBF-FD stencils

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