Presentation | 2006-11-23 Deterministic Network Learning Based on Classification-Error Rate Kar-Ann Toh, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper presents a new error counting network for pattern classification. Two major issues namely, the tedious iterative network learning issue and the ill-posed classification error counting issue have been addressed. Essentially, a linear series functional approximation to network structure and a smooth quadratic error counting function were proposed to resolve these two major computational issues. Our analysis shows that the new error counting objective can be related to the least-squares error objective by adjusting the class-specific normalization factors. The binary classification network is subsequently extended to cater for multi-category problems. An extensive empirical evaluation based on 42 UCI data sets validates the usefulness of the proposed method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Pattern Classification / Neural Networks / Discriminant Functions / Polynomials and Machine Learning |
Paper # | PRMU2006-124 |
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Conference Information | |
Committee | PRMU |
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Conference Date | 2006/11/16(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 | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Deterministic Network Learning Based on Classification-Error Rate |
Sub Title (in English) | |
Keyword(1) | Pattern Classification |
Keyword(2) | Neural Networks |
Keyword(3) | Discriminant Functions |
Keyword(4) | Polynomials and Machine Learning |
1st Author's Name | Kar-Ann Toh |
1st Author's Affiliation | Biometrics Engineering Research Center School of Electrical & Electronic Engineering Yonsei University() |
Date | 2006-11-23 |
Paper # | PRMU2006-124 |
Volume (vol) | vol.106 |
Number (no) | 375 |
Page | pp.pp.- |
#Pages | 10 |
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