Presentation | 2000/6/15 NLP2000-22 / NC2000-16 An Upper Bound on the Size of Min-Max Modular Neural Networks Trainable in Polynomial Time Bao-Liang Lu, Michinori Ichikawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper we present an upper bound on the size of milt-max modular (M^3) neural networks that can be trained in polynomial time for nonlinearly separable problems. Given an arbitrary training set with L training data for a K-class (K>2) pattern classification problem, we show that an M^3 network with [numerical formula] neurons, [numerical formula] MIN units, [numerical formula] MAX units. and ([numerical formula]) INV units, can exactly implement the training set in polynomial time without, learning, where L_i is the number of training data for class C_i for i = 1, …, K. We also show that this M^3 network can perform minimum-distance classifications with respect to finite training data. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Modular neurai network / upper bound on network size / minimum-distance classifer / piece wise linear machine |
Paper # | NLP2000-22,NC2000-16 |
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Conference Information | |
Committee | NLP |
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Conference Date | 2000/6/15(1days) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | NLP2000-22 / NC2000-16 An Upper Bound on the Size of Min-Max Modular Neural Networks Trainable in Polynomial Time |
Sub Title (in English) | |
Keyword(1) | Modular neurai network |
Keyword(2) | upper bound on network size |
Keyword(3) | minimum-distance classifer |
Keyword(4) | piece wise linear machine |
1st Author's Name | Bao-Liang Lu |
1st Author's Affiliation | Lab.for Brain-Operative Device, RIKEN Brain Science Institute() |
2nd Author's Name | Michinori Ichikawa |
2nd Author's Affiliation | Lab.for Brain-Operative Device, RIKEN Brain Science Institute |
Date | 2000/6/15 |
Paper # | NLP2000-22,NC2000-16 |
Volume (vol) | vol.100 |
Number (no) | 124 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |