Presentation 2001/3/15
A method for merging hidden units of RBF networks without relearning of sample patterns
Nobuhiko YAMAGUCHI, Koichiro YAMAUCHI, Naohiro ISHII,
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Abstract(in English) It is well known that we must setup appropriate number of hidden units for neural networks to make the generalization ability of the neural network maximum. In this paper, we propose a method for reducing the number of hidden units for RBF networks, which have already finished the learning of sample patterns. In this method, the RBF network does not relearn the sample patterns during the reduction process, so this method can be applied to several fields, where the system has no capacity to store the sample patterns. For example, this system can be used for reducing the number of hidden units of the network which has learned an environment by reinforcement learning.
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Keyword(in English) Structural Learning / Merge / RBF network / Neural Network
Paper # NC2000-130
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Committee NC
Conference Date 2001/3/15(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A method for merging hidden units of RBF networks without relearning of sample patterns
Sub Title (in English)
Keyword(1) Structural Learning
Keyword(2) Merge
Keyword(3) RBF network
Keyword(4) Neural Network
1st Author's Name Nobuhiko YAMAGUCHI
1st Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology()
2nd Author's Name Koichiro YAMAUCHI
2nd Author's Affiliation Information, Electronics and Systems Engineering, Graduate School of Engineering, Hokkaido University.
3rd Author's Name Naohiro ISHII
3rd Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology
Date 2001/3/15
Paper # NC2000-130
Volume (vol) vol.100
Number (no) 687
Page pp.pp.-
#Pages 7
Date of Issue