Abstract
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.
Cite
CITATION STYLE
Kato, T., Omachi, S., & Aso, H. (2002). Asymmetric Gaussian and its application to pattern recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2396, pp. 405–413). Springer Verlag. https://doi.org/10.1007/3-540-70659-3_42
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
