Abstract
Text categorization has become one of the key techniques for handling and organizing web data. Though the native features of SVM (Support Vector Machines) are better than Naive Bayes’ for text categorization in theory, the classification precision of SVM is lower than Bayesian method in real world. This paper tries to find out the mysteries by analyzing the shortages of SVM, and presents an anti-noise SVM method. The improved method has two characteristics: 1) It chooses the optimal n-dimension classifying hyperspace. 2) It separates noise samples by preprocessing, and trains the classifier using noise free samples. Compared with naive Bayes method, the classification precision of anti-noise SVM is increased about 3 to 9 percent.
This work is supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2003CB314802.
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Chen, L., Huang, J., Gong, ZH. (2005). An Anti-noise Text Categorization Method Based on Support Vector Machines. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds) Advances in Web Intelligence. AWIC 2005. Lecture Notes in Computer Science(), vol 3528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11495772_43
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DOI: https://doi.org/10.1007/11495772_43
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