HYPERTEXT CLASSIFICATION USING WEIGHTED TRANSDUCTIVE SUPPORT VECTOR MACHINES
Hypertext document is a special but important kind of text document for text classification. This paper introduces weighted Transductive Support Vector Machines (WTSVMs),which treat test samples discriminately based on their weight factors rather than treat every test sample equally in Transductive Support Vector Machines (TSVMs). A hybrid similarity function that includes hyperlink and term components is defined and computed, measuring the similarity between an unlabeled sample and labeled documents. Thus,the adjustment of the decision hyper-plane is refined due to reformulating the penalties on unlabeled samples in the training process. Experimental results on benchmark problems show the efficiency of the proposed method.
Transductive support vector machines Text classification Weighted transductive support vector machines Content similarity Hyperlink similarity
SHUANG LIU CHUAN-YING JIA PENG CHEN
Institute of Nautical Science and Technology, Dalian Maritime University, Dalian 116026 P.R.China Department of Computer Science and Technology, Neusoft Institute of Information, Dalian, 116023, P.R
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
3535-3540
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）