已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca

这是人工智能不确定性推理中贝叶斯方法的程序，是用VC++做的。由于是学生实验，所以比较简单。-It is the uncertain reasoning in artificial intelligence--Bayesian approach procedure 。I s done with VC++. As is the student experiment, it is more simple.

贝叶斯推理Bayesian modeling and inference for generalized linear models, accelerated life failure models, Cox regression models and piecewise exponential mode(Bayesian modeling and inference for generalized linear models, accelerated life failure models,