Undirected Markov blanket classifier and integration

The undirected Markov blanket structure is one of the most important dependency structures between attribute and class variables.The key problem of learning undirected Markov blanket classifier is to build the undirected Markov blanket structure.At present,the methods of learning undirected Markov blanket structure are of low efficiency and low reliability and are unpractical.The learning method of both undirected Markov blanket structure and classifier with polynomial complexity is presented based on the theory of Bayesian networks,the theory of Markov networks and the dependency analysis way.The problems above can be avoided.The built optimal theorem,transferable theorem,reliability theorem and local theorem are the theory foundation of the presented method.The approximate learning arithmetic is developed and an undirected Markov blanket classifier is extended as a unite classifier to suit for small data classification.