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New Technology of Library and Information Service  2014, Vol. 30 Issue (9): 74-80    DOI: 10.11925/infotech.1003-3513.2014.09.10
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Study on Improvement of Text Classification Using HS-SVM
Hu Jiming, Chen Guo
Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China
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[Objective] In terms of the class features vector changing and overlapping, this paper improves the classification algorithm conducted by super ball supported vector machine. [Methods] Starting from combing the operational mechanism of LDA and HS-SVM, as well as the related studies, this paper constructs a text classification model based on LDA and HS-SVM. The traditional HS-SVM is improved considering incremental learning and intensive degree, and then the dynamic change of hyper-sphere class' support vector would be achieved and the decision function for constructing hyper-sphere support vector machine would be accurately calculated. [Results] The effect of text classification can be improved from the perspectives of precision rate and recall rate. Comparative experiments are conducted and the results demonstrate that methods in this article are feasible and effective which can effectively improve texts classification. In addition, this method reduces the time of modeling and has little influence on accuracy of predication. [Limitations] Noted that the proposal in this paper is comparatively more complex than the original algorithm that need continuous improvement; and the results needs experiments on more data sets. Meanwhile, the improvement on essence of algorithm is not optimal which is necessary to be further studied. [Conclusions] This study is helpful to improve the accuracy and reduce the training time in large-scale text categorization, and also improve the efficiency and performance of text classification.

Key wordsLDA topic model      Hyper-Sphere Support Vector Machine(HS-SVM)      Incremental learning      Intensive degree decision function     
Received: 04 December 2013      Published: 20 October 2014
:  TP391  

Cite this article:

Hu Jiming, Chen Guo. Study on Improvement of Text Classification Using HS-SVM. New Technology of Library and Information Service, 2014, 30(9): 74-80.

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