bayes网理论及其在在线导购中的应用分析word格式论文.docxVIP

优秀毕业论文 精品参考文献资料 ABSTRACT With the developmemt of electronic commerce ,shopping on-line was accepted by more and more people. The information provided by shopping website is more and more plentiful. A good navigation system for the shopping web site is very important, therefore, how to provide better guiding shopping service is must been solved problem. Application on on-line guiding shopping of Bayesian network can better solve this problem.The belief network(BN) ，proposed by Pearl in1982,also called Bayesian network, probability network, is a new mechanism for knowledge representation based on probability theory and graph theory. Its distinct performance in representing and reasoning about uncertainty makes it a hot research Goods in artificial intelligence. At present the research on belief network include three area: inference over belief network, learning belief network from data, and application of belief network. The research and application for the belief network have gain great progress these years, In this paper, some summary and discussion on the three area were given. Moreover, a mode of on-line guiding shopping of Belief net—works was given. The inference was one of main research about belief network. The junction tree algorithm and bucket elimination algorithm was two important inference algorithm on belief network. The junction tree algorithm has two defect.: the one is that it need too much store space, the other is that it can’t utilized the conditional independence exhibited in the structure of belief network. In this paper, The junction tree algorithm was optimized and then built a new algorithm--the junction tree algorithm based on the bucket elimination. The new algorithm need less store space, and can utilize the conditional independent in a belief network to reduce the computation. The complexity of structure is a main factor in the bucket elimination algorithm. We build a algorithm which can translate a belief network into a new one ba