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Multivariate Structural Bernoulli Mixtures for Recognition of Handwritten Numerals
Barcelona, Spain September 03-September 08
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.90614215th International Conference on Patt ...
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Jirí Grim, Academy of Sciences of the Czech Republic
Pavel Pudil, Academy of Sciences of the Czech Republic
Petr Somol, Academy of Sciences of the Czech Republic
As shown recently, the structural optimization of probabilistic neural networks can be included into EM algorithm by introducing a special type of mixtures. The method has been applied to recognize unconstrained handwritten numerals from the database of Concordia University in Montreal. In the present paper, we discuss the possibility of a proper initialization of EM algorithm for estimating the class-conditional multivariate Bernoulli mixtures.
Citation:
Jirí Grim, Pavel Pudil, Petr Somol, "Multivariate Structural Bernoulli Mixtures for Recognition of Handwritten Numerals," icpr, vol. 2, pp.2585, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 2, 2000
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