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attributes must be shared to have at every node a more accurate estimation of the global classifier. When expanding the knowledge of the local classifiers, to reduce costs, the network traffic should be kept to a minimum. We propose a probabilistic model for a keyword selection method which makes a more thorough analysis
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.These exact word-matching and ranking approaches ignore too many relevant documents that do not contain the exact keywords as specified in a user query
based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision.
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