文档介绍：华中科技大学硕士学位论文Abstract There are many uncertain problems in the real world and some scientific fields. As bination of probability and graph theory, works can use graph theory to picture the structure of the model on one hand. On the other hand, it can make the best of the structure of the model to reduce plexity of the problem under probability rules. So a natural and clear method is developed to deal with uncertain problems. works are widely used in industry, agriculture, medical treatment and national defense, etc, and it has already brought remarkable benefit both in economic and society for us. So it is of great academic meaning and utility value to have a further study of works. The main work and innovations of this paper are as follows: First, the overview of the works. The thesis introduces the background, actuality and application fields, it also summarizes the characteristics of kinds of classification models. After that, the advantages works are pared with other methods. Second, the paper introduces some kinds of learning algorithm about works. In the learning works’ parameter, ponent analysis (PCA) and the idea of works by experts are used to develop a method, which constructs works fully by expert’s knowledge. The method first to get all the parameters from domain experts by a “probability scale”, after determining each expert’s weight by some index, we can get the accurate value by average weight. In the process, PCA is used to deal with the score of each expert, which eliminates the dependence between variables. The method is useful both in declining the influence of subjective factors and improving the result. In the learning work’s structure, a statistic estimator called likelihood-ratio sample-testing is used to construct works. The main idea of this method is to examining the independence between every two variables through the estimator. At last, finance warning is important for enterprise. In this paper, we first use the PCA II华中科技大学硕士学位论文to deal