The experiments show that this algorithm has higher precision compared with simple incremental Bayesian classifier with small training data set and it can reduce largely the computing time that costs in samples optimal selection in incremental learning.
实验表明,在训练数据集较小的情况下,该算法比原增量贝叶斯分类算法具有更高的精度,能大幅度减少增量学习样本优选的计算时间。
参考来源 - 一种基于类支持度的增量贝叶斯学习算法 in C·2,447,543篇论文数据,部分数据来源于NoteExpress
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