Integrating Background Knowledge into RBF Networks for Text Classification
Text classification is a problem applied to natural language texts that assigns a document into one or more predefined categories,based on its content.In this paper,we present an automatic text classification model that is based on the Radial Basis Function (RBF) networks.It utilizes valuable discriminative information in training data and incorporates background knowledge in model learning.This approach can be particularly advantageous for applications where labeled training data are in short supply.The proposed model has been applied for classifying spam email,and the experiments on some benchmark spam testing corpus have shown that the model is effective in learning to classify documents based on content and represents a competitive alternative to the well-known text classifiers such as naive Bayes and SVM.
Radial basis function networks text classification clustering information retrieval
Eric P.Jiang
University of San Diego,5998 Alcala Park San Diego,California 92110,United States of America
国际会议
4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)
哈尔滨
英文
61-70
2008-01-16（万方平台首次上网日期，不代表论文的发表时间）