Document classification algorithm based on kernel logistic regression

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 can offer better performance for document classification in comparison with ordinary classification algorithms.