DocumentCode
2478168
Title
A new HMM training and testing scheme
Author
Ko, Albert Hung-Ren ; Sabourin, Robert ; de Souza Britto, A.
Author_Institution
Univ. of Toronto, Toronto, ON, Canada
fYear
2008
fDate
8-11 Dec. 2008
Firstpage
1
Lastpage
4
Abstract
One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually ¿break¿ a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment on handwritten digit recognition problem and the result suggests our proposal can be as effective as multi classifier systems.
Keywords
handwritten character recognition; hidden Markov models; learning (artificial intelligence); pattern classification; HMM training; handwritten digit recognition problem; hidden Markov model; multiclassifier system; observation sequences; unexpected noises; Databases; Handwriting recognition; Hidden Markov models; Mathematical model; NIST; Pattern recognition; Proposals; Stochastic processes; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location
Tampa, FL
ISSN
1051-4651
Print_ISBN
978-1-4244-2174-9
Electronic_ISBN
1051-4651
Type
conf
DOI
10.1109/ICPR.2008.4761250
Filename
4761250
Link To Document