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 ) 針對已有的學習算法中存在的效率不高及部分組合意義不大的問題,本文提出了條件互信息度量半樸素貝葉斯分類學習算法( 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.