Support Vector Machines Based on a Semantic Kernel for Text Categorization

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.

• Type: Conference
• Year: 2000
• Where: IJCNN
• Authors: George Siolas, Florence d'Alché-Buc
• Keywords: IJCNN 2000, Neural Networks, Priori Semantic Knowledge, Support Vector Machines, Text Categorization Task