Please wait a minute...
New Technology of Library and Information Service  2005, Vol. 21 Issue (12): 44-47    DOI: 10.11925/infotech.1003-3513.2005.12.10
Current Issue | Archive | Adv Search |
Research on Two Classes Text Categorization Method Based on an Improved Support Vector Machine
Ying Wei1   Wang Zhengou1   An Jinlong2
1 (Institute of Systems Engineering, Tianjin University, Tianjin 300072, China)
2 (Hebei University of Technology, Tianjin 300130, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

This paper puts forward a method of two text categorization classes based on the pre-extracting support vectors and fuzzy circulated iterative algorithm. Compared with the conventional Support Vector Machines(SVM), the present method possesses much higher computation efficiency. This paper gives the concrete procedure of the algorithm, and applies it to the text classification. Experimental results demonstrate the effectiveness and the efficiency of the approach.

Key wordsText categorization      Support Vector Machines(SVM)      Pre-extracting support vectors      Fuzzy circulated iterative algorithm     
Received: 29 August 2005      Published: 25 December 2005
: 

G254.1

 
Corresponding Authors: Ying Wei     E-mail: nobertying@126.com
About author:: Ying Wei,Wang Zhengou,An Jinlong

Cite this article:

Ying Wei,Wang Zhengou,An Jinlong. Research on Two Classes Text Categorization Method Based on an Improved Support Vector Machine. New Technology of Library and Information Service, 2005, 21(12): 44-47.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2005.12.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2005/V21/I12/44

1Talwar V, Mitra P.  Web mining in soft computing framework: relevance, state of the art and future directions. Neural Networks, IEEE Transactions on , Volume: 13 , Issue: 5 , Sep 2002, 1163-1177
2Chih-Wei Hsu, Chih-Jen Lin.  A comparison of methods for multiclass support vector machines.  IEEE Transactions on Neural Networks, 2002, 13(2):415-425
3安金龙,王正欧. 一种新的支持向量机多类分类方法. 信息与控制,2004,(3):262-267
4史忠植. 知识发现. 北京 清华大学出版社,2002
5Wemter G, Arevian, and C pancjev.  Recurrent Neural Network Learing for Text Routing. Proceedings of the International Conference on Artificial Neural Network. Edinburgh, UK,1999, 898-903
6Rennie and Ryan Rifkin.  Improving Multiclass Text Classification with the Support Vector Machine\
[DB/OL\]. Online at: \
[www.ai.mit.edu/research/abstractabstracts2001/machine-learning\] Available: May 23,2002
7Joachims T.  Text categorization with Support Vector Machines:Learning with Many Relevant Features. Proceedings10th European Conference on Machine Learning.1998, ECML-98, 137-142
8Susan Dumais, John Platt, David Hekerman, and M Sahami.  Inductive Learning Algorithms and Representations for text Categorization. 7th International Conference on Information and Knowledge Management, 1998
9Bssu A , Watters C, Shepherd M.  Support Vector Machines for Text categorization . System Sciences, proceedings of the 36th Annual Hawaii International, 6-9Jan, 2003, 7-13
10王明春,王正欧, 张楷等.  一种基于CHI值特征选取的粗糙集文本分类规则抽取方法. 计算机应用, 2005(5):1026-1028
11Vladimir N Vapnik.  An overview of Statistical Learning Theory. IEEE Transactions on Neural Networks, 1999,10(5): 988-999
12Nello Cristianini & John Shawe-Taylor.  An Introduction To Support Vector Machines.New York, USA, Cambridge University Press,2000
13Vasehgi S V.  State duration modeling in hidden Markov models. Signal processing, 1995, (41):31-41
14Vladimir N, Vapnik.  Statistical Learning Theory. Wiley-Interscience Publication, John Wiley&Sons,Inc.New York, USA,1998
15Mokhtar Bazaraa, Hanif Sherali, and Shetty.  Nonlinear Programming: Theory and Algorithms,2nd Edition.Hamilton printing, John Wiley&Sons, Inc.New York, USA, 1993
16安金龙,王正欧. 一种适合于增量学习的支持向量机的快速循环算法. 计算机应用, 2003,23(10):12-14
17安金龙, 王正欧. 预抽取支持向量机的支持向量. 计算机工程, 2004, 30 (10): 10-12
18Zhang Li, Zhou Weida, Jiao Licheng.  Pre-extracting Support Vectors fof Support Vector    Machine. Signal Processing Proceedings, 2000 (3):1432-1435
19Cortes C, Vladimir N Vapnik.  Support Vector Networks. Machine Learning, 1995 (20):273-297
20John Platt.  Fast Training of Support Vector Machines using Sequential Minimal Optimization. Advances in Kernel Methods-Support Vector Learning. Cambridge, MA, MIT Press, 1999,185-208
21Christopher Burges.  A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 1998, 2(2):121-167

[1] Zhanglu Tan,Zhaogang Wang,Han Hu. Study on a Method of Feature Classification Selection Based on χ2 Statistics[J]. 数据分析与知识发现, 2019, 3(2): 72-78.
[2] Li Xiangdong,Gao Fan,Li Youhai. Categorizing Documents Automatically within Common Semantic Space[J]. 数据分析与知识发现, 2018, 2(9): 66-73.
[3] Feng Guoming,Zhang Xiaodong,Liu Suhui. Classifying Chinese Texts with CapsNet[J]. 数据分析与知识发现, 2018, 2(12): 68-76.
[4] Xu Dongdong, Wu Shaobo. An Improved TF-IDF Feature Selection Based on Categorical Description[J]. 现代图书情报技术, 2015, 31(3): 39-48.
[5] Tan Xueqing, Zhou Tong, Luo Lin. A Text Classification Algorithm Based on the Average Category Similarity[J]. 现代图书情报技术, 2014, 30(9): 66-73.
[6] Li Xiangdong, He Haihong, Cao Huan, Huang Li. An Algorithm of Digital Resources Text Categorization for Training Sets Skewed Distribution[J]. 现代图书情报技术, 2014, 30(7): 24-33.
[7] Li Xiangdong, Liao Xiangpeng, Huang Li. Research and Implementation of Bibliographic Information Classification System in LDA Model[J]. 现代图书情报技术, 2014, 30(5): 18-25.
[8] Lu Yonghe, Liang Minghui. Improvement of Text Feature Extraction with Genetic Algorithm[J]. 现代图书情报技术, 2014, 30(4): 48-57.
[9] Wang Hao, Ye Peng, Deng Sanhong. The Application of Machine-Learning in the Research on Automatic Categorization of Chinese Periodical Articles[J]. 现代图书情报技术, 2014, 30(3): 80-87.
[10] Lu Yonghe, Li Yanfeng. A Feature Selection Based on Consideration of Multiple Factors[J]. 现代图书情报技术, 2013, (5): 34-39.
[11] Qu Peng, Wang Huilin. Fundamental Research Questions in Patent Text Categorization[J]. 现代图书情报技术, 2013, 29(3): 38-44.
[12] Xu Kun, Cao Jindan, Bi Qiang. A Study and Application on Medical Text Categorization Based on FCA[J]. 现代图书情报技术, 2012, 28(3): 23-26.
[13] Lu Yonghe, He Xinyu. An Application of Sharpen Gaussian Template in a Text Feature Weight Adjustment Methodology[J]. 现代图书情报技术, 2012, (12): 39-44.
[14] Lu Yonghe, Cao Lichao. Text Feature Selection Method Based on Particle Swarm Optimization[J]. 现代图书情报技术, 2011, 27(7/8): 76-81.
[15] Ma Fang. Research of Patent Automatic Classification Based on RBFNN[J]. 现代图书情报技术, 2011, 27(12): 58-63.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn