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Document feature extraction and classifier selection are two key problems for document classification approach. To effectively resolve the above two problems, a novel document classification algorithm is proposed by combining the merits of local fisher discriminant analysis and kernel logistic regression. Extensive experiments have been conducted, and the results demonstrate that the proposed algorithm...
Document classification has received extensive attention in the past few decades due to its wide applications in many fields. To efficiently deal with this problem, a novel document classification algorithm based on information bottleneck (IB) and least square version of SVM (LS-SVM) is proposed in this paper. Extensive experimental results on the real-word document corpus show that the proposed algorithm...
With many potential applications in document management and Web searching, document classification has recently gained more attention. To efficiently resolve this problem, an efficient document classification algorithm based on neighborhood preserving embedding (NPE) and particle swarm optimization (PSO) is proposed in this paper. The document features are first extracted by the NPE algorithm, then...
With the explosive growth in the Web documents, classifying document from the large-scale document database has become one of the most active research fields in data mining communities. Thus, developing an efficient document categorization algorithm to automatically classify Web document is of great importance. In this paper, an efficient document classification algorithm with shuffled frog leaping...
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