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2009 WRI World Congress on Computer Science and Information Engineering
A Simplified Learning Algorithm of Incremental Bayesian
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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, vol. 5, pp.126-128, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
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