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Support Vector Machines based on a semantic kernel for text categorization

Georges Siolas 1 Florence d'Alché-Buc 1 
1 APA - Apprentissage et Acquisition des connaissances
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : 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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Submitted on : Monday, August 7, 2017 - 5:00:15 PM
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Georges Siolas, Florence d'Alché-Buc. Support Vector Machines based on a semantic kernel for text categorization. IEEE-IJCNN'2000, Jul 2000, Come, Italy. pp.205-209, ⟨10.1109/IJCNN.2000.861458⟩. ⟨hal-01572559⟩



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