DocumentCode :
2611709
Title :
Combining global and local classifiers with Bayesian network
Author :
Matos, Leonardo Nogueira ; De Carvalho, João Marques
Author_Institution :
Federal University of Sergipe, Brazil
Volume :
4
fYear :
2006
fDate :
20-24 Aug. 2006
Firstpage :
952
Lastpage :
952
Abstract :
This paper introduces a classification method based on feature space segmentation. Since the classification task is equivalent to a probability distribution estimation, a Bayesian network is used as an inference mechanism for dealing with the underling probability distribution function that, presumably, is complex and factored. The article presents a method for splitting the feature space into regions that are associated to local classifiers. After that, a Bayesian network is used for combining their outputs. Experimental results reveal that this is a suitable approach for speeding up the training phase for large databases as well as to ensure good recognition rates.
Keywords :
Bayesian methods; Computer science; Distributed computing; Equations; Inference mechanisms; Multidimensional systems; Optical character recognition software; Pattern recognition; Probability distribution; Spatial databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.385
Filename :
1699998
Link To Document :
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