Presentation 1994/3/25
A realization method of otimally generalizing neural network based on error minimization
Hidemitsu Ogawa, Jun-ichi Funada,
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Abstract(in English) The task required of a Multilayer Perceptron can be summarised as an optimal approximation of the original function from the set of sampled data,incorporating the concept of generalization ability.Based on previous research,it has been shown that even if the original function is not known,it′s best approximation can be computed using the technique namely Projection Generalizing Neural Network(PGNN).However,using the above technique,we obtain a family of NNs realizing the same goal.In this paper,an effort has been made to exploit this existing degree of freedom.While maintaining the optimal generalizing abilty of the net,a method of generating a NN which is least susceptible to errors in the connection weights is theorized.
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Keyword(in English) multilayer perceptron / generalization / projection learning / projection generalizing heural network / PGNN / minimum error realization
Paper # NC93-127
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Committee NC
Conference Date 1994/3/25(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A realization method of otimally generalizing neural network based on error minimization
Sub Title (in English)
Keyword(1) multilayer perceptron
Keyword(2) generalization
Keyword(3) projection learning
Keyword(4) projection generalizing heural network
Keyword(5) PGNN
Keyword(6) minimum error realization
1st Author's Name Hidemitsu Ogawa
1st Author's Affiliation Department of Computer Science,Faculty of Engineering,Tokyo Institute of Technology()
2nd Author's Name Jun-ichi Funada
2nd Author's Affiliation Department of Computer Science,Faculty of Engineering,Tokyo Institute of Technology
Date 1994/3/25
Paper # NC93-127
Volume (vol) vol.93
Number (no) 537
Page pp.pp.-
#Pages 8
Date of Issue