Research on Learning Bayesian Network and Its Application in Text Detections
Liangmei Hu · Fudan xuebao. Ziran Kexue ban · 2004
A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network. Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.Then, the train data set is changed from a single table to a database composed of some database tables. And each database table corresponds to a Bayesian network block. Based on that, a formula of conditional probability is developed. And each conditional probabilistic table of Bayesian network block can be calculated from the database tables respectively. Properly adjust the attribute number in each database table can assure the validity of this learning approach. Finally, experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.