Presentation | 2001/3/16 Learning of Strongly Correlated Pattern Distribution by Stochastic Feed-Forward Neuralnetworks Sumiyoshi Fujiki, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The learrning ability of the stochastic feed-forward neuralnetwork for the probability distribution of strongly correlated patterns is studied. By using the Kullback's measure the learning process of the inverse XOR problem is studied by the 2-m-2 structure networks with two types of synaptic connections, the one with nearest neighbor interlayer connections only and the other with far neighbor interlayer connections. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Layered Neural Network / Distribution of Strongly Correlated Patterns / iXOR problem |
Paper # | NC2000-161 |
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Conference Information | |
Committee | NC |
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Conference Date | 2001/3/16(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning of Strongly Correlated Pattern Distribution by Stochastic Feed-Forward Neuralnetworks |
Sub Title (in English) | |
Keyword(1) | Layered Neural Network |
Keyword(2) | Distribution of Strongly Correlated Patterns |
Keyword(3) | iXOR problem |
1st Author's Name | Sumiyoshi Fujiki |
1st Author's Affiliation | Tohoku Bunka Gakuen University() |
Date | 2001/3/16 |
Paper # | NC2000-161 |
Volume (vol) | vol.100 |
Number (no) | 688 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |