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Los Angeles, California USA

Mar. 31, 2009 to Apr. 2, 2009

ISBN: 978-0-7695-3507-4

pp: 126-128

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.305

ABSTRACT

A proportion factor is constructed though the Maximum Aposteriori Probability of examples in test data to select the training examples in incremental learning process. Instead of complex normal classify loss expression, the proportion factor λ is used to estimate the classify loss to improve classification efficiency. The final experiment shows that this algorithm is feasible, and more accurate than simple Bayesian classifier. The computing time is highly reduced on the optimal selection of examples in incremental learning process.

INDEX TERMS

Bayesian classifier, simplified algorithm, incremental learning

CITATION

Chen Hua,
Zhang Xiao-gang,
Zhang Jing,
Ding Li-hua,
"A Simplified Learning Algorithm of Incremental Bayesian",

*CSIE*, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 126-128, doi:10.1109/CSIE.2009.305