Presentation | 1998/5/25 Recognition of particular patterns and each of its position in an image, by an integrated neural network consisting of many unit-networks of identity transformation Masahiro Ishido, Shuhei Takagi, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | The integrated neural network consists of many unit-networks of five layered neural network of identity transformation, connected two-dimensionally in parallel in order to deal with an image. The unit-network learns some particular patterns and is copied, and then they are connected to construct the integrated network. When an image containing particular patterns and others is given to the input units, only the particular patterns appear on the output image at the same position. A new pattern and its position is recognized by connecting another unit-network in parallel which has learned the new pattern. It is not needed that the previous unit-networks learn it. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Identity transformation / Inner state / Generalization / BP learning |
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Committee | NC |
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Conference Date | 1998/5/25(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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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) | Recognition of particular patterns and each of its position in an image, by an integrated neural network consisting of many unit-networks of identity transformation |
Sub Title (in English) | |
Keyword(1) | Identity transformation |
Keyword(2) | Inner state |
Keyword(3) | Generalization |
Keyword(4) | BP learning |
1st Author's Name | Masahiro Ishido |
1st Author's Affiliation | Department of Computer and Systems Engineering, Kobe University() |
2nd Author's Name | Shuhei Takagi |
2nd Author's Affiliation | Department of Computer and Systems Engineering, Kobe University |
Date | 1998/5/25 |
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Volume (vol) | vol.98 |
Number (no) | 77 |
Page | pp.pp.- |
#Pages | 6 |
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