Presentation 2002/1/22
Mixture of Asymmetric Gaussians and Its Maximum Likelihood Estimation
Tsuyoshi KATO, Shinichiro OMACHI, Hirotomo ASO,
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Abstract(in English) In this paper, we propose a new probability model, 'asymmetric Gaussian(AG)', which is an extension of Gaussian. The AG can capture spatially asymmetric distributions, yet remaining computationally tractable. There is a similar work [1], which introduces 'Asymmetric Mahalanobis Distance (AMD)' and applys it to handwritten Chinese and Japanese character recognition. The AMD can measure a spatially asymmetrical distance between an unknown pattern and a category class and shows excellent classification performance. However, the AMD is suitable for only an unimodal distribution, so the range of its application is necessarily somewhat limited. Meanwhile, since our model is formulated by a density function, it is easy to be extended to mixture of asymmetric Gaussians (MAG), which can capture multimodal distributions. We apply the AGs to character classification problem and show that the AGs outperform Gaussian models.
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Keyword(in English) Asymmetric Gaussian / Mixture of Asymmetric Gaussians / Expectation-Conditional Maximization algorithm / Character recognition
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Committee NC
Conference Date 2002/1/22(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Mixture of Asymmetric Gaussians and Its Maximum Likelihood Estimation
Sub Title (in English)
Keyword(1) Asymmetric Gaussian
Keyword(2) Mixture of Asymmetric Gaussians
Keyword(3) Expectation-Conditional Maximization algorithm
Keyword(4) Character recognition
1st Author's Name Tsuyoshi KATO
1st Author's Affiliation Graduate School of Engineering, Tohoku University()
2nd Author's Name Shinichiro OMACHI
2nd Author's Affiliation Graduate School of Engineering, Tohoku University
3rd Author's Name Hirotomo ASO
3rd Author's Affiliation Graduate School of Engineering, Tohoku University
Date 2002/1/22
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Volume (vol) vol.101
Number (no) 616
Page pp.pp.-
#Pages 6
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