Since most algorithms are not effective and not very meaningful in combining , this thesis proposes an algorithm based on a kind of semi - naive bayesian classifier which is measured by conditional mutual information ( cmi - bsnbc ) 针对已有的学习算法中存在的效率不高及部分组合意义不大的问题,本文提出了条件互信息度量半朴素贝叶斯分类学习算法( cmi - bsnbc ) 。
In probability theory, and in particular, information theory, the conditional mutual information is, in its most basic form, the expected value of the mutual information of two random variables given the value of a third.