A Simplified Learning Algorithm of Incremental Bayesian
Hua Chen, Xiaogang Zhang, Jing Zhang, Ding Li-hua · 2009
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 lambda 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.