Asymmetric Gaussian and Its Application to Pattern Recognition

  • Tsuyoshi Kato
  • Shinichiro Omachi
  • Hirotomo Aso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


In this paper, we propose a new probability model, ‘asymmetric Gaussian(AG),’ which can capture spatially asymmetric distributions. It is also extended to mixture of AGs. The values of its parameters can be determined by Expectation-Conditional Maximization algorithm. We apply the AGs to a pattern classification problem and show that the AGs outperform Gaussian models.


Mixture Model Character Recognition Latent Variable Model Orthonormal Matrix Handwritten Character Recognition 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Tsuyoshi Kato
    • 1
  • Shinichiro Omachi
    • 1
  • Hirotomo Aso
    • 1
  1. 1.Graduate School of EngineeringTohoku UniversitySendai-shiJapan

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