DocumentCode
1743005
Title
Multivariate structural Bernoulli mixtures for recognition of handwritten numerals
Author
Grim, JiYí ; Pudil, Pavel ; Somol, Petr
Author_Institution
Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic
Volume
2
fYear
2000
fDate
2000
Firstpage
585
Abstract
The structural optimization of a probabilistic neural network can be included into an expectation maximisation (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. We discuss the possibility of a proper initialization of the EM algorithm for estimating the class-conditional multivariate Bernoulli mixtures
Keywords
character recognition; maximum likelihood estimation; neural nets; optimisation; probability; class-conditional multivariate Bernoulli mixtures; expectation maximisation algorithm; multivariate structural Bernoulli mixtures; probabilistic neural network; structural optimization; unconstrained handwritten numerals; Automation; Databases; Handwriting recognition; Information theory; Input variables; Iterative algorithms; Neural networks; Neurons; Probability distribution; Structural engineering;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location
Barcelona
ISSN
1051-4651
Print_ISBN
0-7695-0750-6
Type
conf
DOI
10.1109/ICPR.2000.906142
Filename
906142
Link To Document