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Support Vector Machines based on a semantic kernel for text categorization

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2 Author(s)
G. Siolas ; Lab. d'Inf., Univ. Pierre et Marie Curie, Paris, France ; F. d'Alche-Buc

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20-newsgroups database. Support Vector Machines provide the best accuracy on test data

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Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on  (Volume:5 )

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