Presentation | 1994/10/13 Construction of Classification Trees Based on Features Provided by Random MLP Qiangfu Zhao, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Recently,the author has shown that random multilayer perceptrons with cross-layer connections (CLC-MLP) could be used as general purpose feature extractors,and complex patterns could be mapped into linearly separable ones.To gain some insight into the properties of random CLC-MLP,this paper investigates the goodness of features provided by random CLC-MLP using classification trees. A simple method is first introduced to construct binary trees for recognition of binary image patterns.Then,the classification tree approach is applied to invariant recognition of numerics (0-8). Experimental results show that useful features can be extracted automatically by using random CLC-MLP,and the size of classification trees can be greatly reduced. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Multilayer perceptron / Classification tree / Cross-Layer Connections / Feature extraction / Pattern recognition |
Paper # | NC94-35 |
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Conference Information | |
Committee | NC |
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Conference Date | 1994/10/13(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Construction of Classification Trees Based on Features Provided by Random MLP |
Sub Title (in English) | |
Keyword(1) | Multilayer perceptron |
Keyword(2) | Classification tree |
Keyword(3) | Cross-Layer Connections |
Keyword(4) | Feature extraction |
Keyword(5) | Pattern recognition |
1st Author's Name | Qiangfu Zhao |
1st Author's Affiliation | Graduate School of Information Sciences,Tohoku University() |
Date | 1994/10/13 |
Paper # | NC94-35 |
Volume (vol) | vol.94 |
Number (no) | 272 |
Page | pp.pp.- |
#Pages | 7 |
Date of Issue |