Presentation | 2005/3/23 Multi-class Pattern Classification based on a Probabilistic Model of Combining Binary Classifiers Naoto YUKINAWA, Shigeyuki OBA, Kikuya KATO, Shin ISHII, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose a novel probabilistic model for constructing a multi-class pattern classifier by weighted aggregation of binary classifiers, which has latent variables as class membership probabilities. We also derive a maximum likelihood algorithm to estimate the latent probability and hence the class membership. We apply our method to classification problems of synthetic data and a real world tumor data. We show that our method can achieve the comparative performance to heuristic voting methods. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | multi-class classification / binary classifier / gene expression analysis |
Paper # | NC2004-221 |
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Committee | NC |
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Conference Date | 2005/3/23(1days) |
Place (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) | Multi-class Pattern Classification based on a Probabilistic Model of Combining Binary Classifiers |
Sub Title (in English) | |
Keyword(1) | multi-class classification |
Keyword(2) | binary classifier |
Keyword(3) | gene expression analysis |
1st Author's Name | Naoto YUKINAWA |
1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
2nd Author's Name | Shigeyuki OBA |
2nd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
3rd Author's Name | Kikuya KATO |
3rd Author's Affiliation | Research Institute, Osaka Medical Center for Cancer and Cardiovascular Diseases |
4th Author's Name | Shin ISHII |
4th Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
Date | 2005/3/23 |
Paper # | NC2004-221 |
Volume (vol) | vol.104 |
Number (no) | 760 |
Page | pp.pp.- |
#Pages | 6 |
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