Presentation | 1997/12/18 Probabilistic Descent Method Applied to Similarity and Distance Measure of Quadratic Form for Pattern Recognition Yoshiaki Kurosawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Probabilistic Descent (PD) was proposed by Amari and has been known as a method of the competitive learning in the statistical pattern recognition methods. This was expanded to GPD by Katagiri for flexible length vector data where the idea proposed by Amari was applied to fixed length vector data. The other competitive learning methods, LVQ, LSM, and ALSM were proposed by Kohonen and have been well known as effective methods for pattern recognition. Theoretical relationship between these methods are discussed in this report and the new approach for a learning method in this field is proposed. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pattern Recognition / Character Recognition / PD / GPD / Probabilistic Descent |
Paper # | PRMU97-181 |
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Committee | PRMU |
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Conference Date | 1997/12/18(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Probabilistic Descent Method Applied to Similarity and Distance Measure of Quadratic Form for Pattern Recognition |
Sub Title (in English) | |
Keyword(1) | Pattern Recognition |
Keyword(2) | Character Recognition |
Keyword(3) | PD |
Keyword(4) | GPD |
Keyword(5) | Probabilistic Descent |
1st Author's Name | Yoshiaki Kurosawa |
1st Author's Affiliation | Toshiba Research and Development Center() |
Date | 1997/12/18 |
Paper # | PRMU97-181 |
Volume (vol) | vol.97 |
Number (no) | 458 |
Page | pp.pp.- |
#Pages | 8 |
Date of Issue |