Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.305
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.
Bayesian classifier, simplified algorithm, incremental learning
Chen Hua, Zhang Xiao-gang, Zhang Jing, Ding Li-hua, "A Simplified Learning Algorithm of Incremental Bayesian", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 126-128, doi:10.1109/CSIE.2009.305