A Discriminant Function Based on Feature Transformation Considering Normality Improvement of Distribution
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概要
- 論文の詳細を見る
In statistical pattern recognition, class conditional probability distribution is estimated and used for classification. Since it is impossible to estimate the true distribution, usually the distribution is assumed to be a certain parametric model like normal distribution and the parameters that represent the distribution are estimated from training data. In this paper, we propose a method to improve classification accuracy by transforming the distribution of the given data closer to the normal distribution using data transformation. We show how to modify the conventional quadratic discriminant function (QDF) in order to deal with the transformed data.
- 社団法人電子情報通信学会の論文
- 2003-04-01
著者
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Omachi Shinichiro
Graduate School Of Engineering Tohoku University
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ASO Hirotomo
Graduate School of Engineering, Tohoku University
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Aso Hirotomo
Graduate School Of Engineering Tohoku University
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UJIIE Hidenori
Graduate School of Engineering,Tohoku University
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Ujiie Hidenori
Graduate School Of Engineering Tohoku University
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ASO Hirotomo
Graduate School of Engineering,Tohoku University
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