Title :
Support Vector Machines based on a semantic kernel for text categorization
Author :
Siolas, Georges ; Buc, Florence D Alché
Author_Institution :
Lab. d´´Inf., Univ. Pierre et Marie Curie, Paris, France
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
Keywords :
classification; learning (artificial intelligence); radial basis function networks; text analysis; K-nearest neighbors algorithm; Support Vector Machines; learning; metric; newsgroups database; radial basis kernels; semantic kernel; semantic knowledge; text categorization; Databases; Frequency; Inference algorithms; Kernel; Learning systems; Machine learning algorithms; Support vector machine classification; Support vector machines; Testing; Text categorization;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.861458
