Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training

朴素贝叶斯分类
..............\BaysClass
..............\.........\BaysClass.cpp
..............\.........\BaysClass.dsp
..............\.........\BaysClass.dsw
..............\.........\BaysClass.ncb
..............\.........\BaysClass.opt
..............\.........\BaysClass.plg
..............\.........\Debug
..............\.........\ReadMe.txt
..............\.........\StdAfx.cpp
..............\.........\StdAfx.h
..............\朴素贝叶斯分类.doc

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