<-上一篇/Previous Article 下一篇/Next Article->

[1]古丽娜孜,孙铁利,伊力亚尔,等.一种基于主动学习支持向量机哈萨克文文本分类方法[J].智能系统学报,2011,6(3):261-267.
　GU Linazi,SUN Tieli,YI Liyaer,et al.An approach to the text categorization of the Kazakh language based on an active learning support vector machine[J].CAAI Transactions on Intelligent Systems,2011,6(3):261-267.

点击复制

一种基于主动学习支持向量机哈萨克文文本分类方法

PDF下载 HTML

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 6 期数: 2011年第3期页码: 261-267 栏目: 学术论文—自然语言处理与理解出版日期: 2011-06-25

Title:: An approach to the text categorization of the Kazakh language based on an active learning support vector machine

文章编号:: 1673-4785(2011)03-0261-07

作者:: 古丽娜孜^1，2，孙铁利²，伊力亚尔¹,吴迪²; 1.伊犁师范学院电子与信息工程学院，新疆伊宁 835000；
2.东北师范大学计算机科学与信息技术学院,吉林长春 130117

Author(s):: GU Linazi^1,2 , SUN Tieli², YI Liyaer¹, WU Di^2; 1.School of Electronic and Information Engineering, Yili Normal University, Yining 835000, China；
2.School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China

关键词:: 支持向量机; 哈萨克文文本分类; 主动学习

Keywords:: support vector machine; Kazakh text categorization; active learning

分类号:: TP391.1

文献标志码:: A

摘要:: 将文本分类理论应用于哈萨克语中，给出基于支持向量机的哈萨克文文本分类系统的设计思想.从哈萨克语言学的角度对哈萨克文分析，提出哈萨克文词干提取的方法.在对支持向量机的理论分析基础上，提出主动学习算法对支持向量机进行训练，使用训练后的分类器对新的文本进行分类.实验结果表明，该方法在哈萨克文文本分类中能获得可接受的分类性能.

Abstract:: In applying the theory of text categorization to the study to the Kazakh language, an approach to text categorization of Kazakh text based on a support vector machine system was introduced. In this paper, from the Kazakh linguistic angle, the method to extract word stems was analyzed. Based on analysis of the support vector machine, the proposed active learning algorithm was adopted for training. The trained classifier was used to classify new text. The experimental results show that this approach to Kazakh text classification has an acceptable classification performance.

参考文献/References:: ［1］LV lin, LIU Yushu. Research of English text classification methods based on semantic meaning［C］//2005 International Conference on Information and Communication Technologies. Karachi, Pakistan, 2005: 689700.
［2］陈立伟，井志强，葛秘蕾.基于特征项扩展的中文文本分类方法［J］.应用科技, 2010, 37(3): 15.
?CHEN Liwei, JING Zhiqiang, GE Milei.A Chinese text classification method based on feature expansion［J］.Applied Science and Technology, 2010, 37(3): 15.
［3］张彰,樊孝忠.一种改进的基于SVM的文本分类算法［J］.计算机工程与设计, 2006, 27(21): 40784080．
?ZHANG Zhang, FAN Xiaozhong. Improved VSM based on Chinese text categorization［J］. Computer Engineering and Design, 2006, 27(21): 40784080.
［4］JOACHIMS T. Text categorization with support vector machines: learning with many relevant features［C］//Proceedings of the 10th European Conference on Machine Learning(ECML98). Chemnitz, Germany: Springer Verlag, 1998: 137142.
［5］VAPNIK V N. Statistical learning theory［M］. New York, USS: John Wiley & Sons Inc, 1998: 375570.
［6］SEBASTIANI F. Machine learning in automated text categorization［J］. ACM Computing Surveys, 2002, 34(1): 147.
［7］TONG S, KOLLER D. Support vector machine active learning with applications to text classification［J］. Journal of Machine Learning Research, 2001, 2(1): 4566.
［8］BURGES J C. A tutorial on support vector machines for pattern recognition［J］. Data Mining and Knowledge Discovery, 1998, 2(2): 12l167.
［9］朱红斌,蔡郁.基于主动学习支持向量机的文本分类［J］.计算机工程应用, 2009, 45(2): 134136.
ZHU Hongbin, CAI Yu. Text categorization based on active learning support vector machines［J］. Computer Engineering and Applications, 2009, 45(2): 134136.
［10］刘宏,屠轶清,黄上腾.一种新的支持向量机主动学习策略及其在文本分类中的应用［J］.计算机科学, 2003, 30(6): 110112.
LIU Hong, TU Zhiqing, HUANG Shangteng. A new support vector machines active learning approach and its application in text classification［J］. Computer Science, 2003, 30(6): 110112.
［11］刘志刚,李德仁,秦前清,等.支持向量机在多类分类问题中的推广［J］.计算机工程与应用, 2004, 40(7): 1013, 65.
LIU Zhigang, LI Deren, QIN Qianqing, et al. An analytical overview of methods for multicategory with support vector machines［J］. Computer Engineering and Applications, 2004, 40(7): 1013, 65.
［12］张健沛,徐华.支持向量机(SVM)主动学习方法研究与应用［J］.计算机应用, 2004(1): 13.
ZHANG Jianpei, XU Hua. Study and application of active learning with SVM［J］. Computer Applications, 2004(1): 13.
［13］PLATT J C, CRISTIANINI N, SHAWETAYLOR J. Large margin DAGs for multiclass classification［C］//Proceedings of Neural Information Processing Systems. Cambridge, USA: MIT Press, 2000: 547553.
［14］贺慧,王俊义.主动支持向量机的研究及其在蒙文文本分类中的应用［J］.内蒙古大学学报:自然科学版, 2006, 37(5): 560563. HE Hui, WANG Junyi. Study of active learning support vector machine and its application on Mongolian text classification［J］. Acta Sientiarum Naturalium Universitatis NeiMongol, 2006, 37(5): 560563.
［15］SCHOHN G, COHN D. Less is more: active learning with support vector machines［C］//Proceedings of the Seventeenth International Conference on Machine Learning (ICML2000). Stanford, USA, 2000: 839846.

相似文献/References:: [1]王书舟,伞冶.支持向量机的训练算法综述[J].智能系统学报,2008,3(6):467.
　WANG Shu-zhou,SAN Ye.A survey on training algorithms for support vector machine[J].CAAI Transactions on Intelligent Systems,2008,3():467.
[2]陈小娥,陈昭炯.多类SVM在图像艺术属性分类中的应用研究[J].智能系统学报,2009,4(2):157.
　CHEN Xiao-e,CHEN Zhao-jiong.An application of multiclass SVM in the classification of artistic attributes of images[J].CAAI Transactions on Intelligent Systems,2009,4():157.
[3]黄剑华,唐降龙,刘家锋,等.一种基于Homogeneity的文本检测新方法[J].智能系统学报,2007,2(1):69.
　HUANG Jian-hua,TANG Xiang-long,LIU Jia-feng,et al.A new method for text detection based on Homogeneity[J].CAAI Transactions on Intelligent Systems,2007,2():69.
[4]张亮,朱振峰,赵耀,等.基于镜头的鲁棒视频广告检测[J].智能系统学报,2007,2(2):83.
　ZHANG Liang,ZHU Zhen-feng,ZHAO Yao,et al.Video commercial detection based on the robustness of sho t[J].CAAI Transactions on Intelligent Systems,2007,2():83.
[5]赵春晖,陈万海,万? 建.一种改进的多类支持向量机超光谱图像分类方法[J].智能系统学报,2008,3(1):77.
　ZHAO Chun-hui,CHEN Wan-hai,WAN jian.An improved hyperspectral image classification method for? a multiclass support vector machine[J].CAAI Transactions on Intelligent Systems,2008,3():77.
[6]杨志豪,洪　莉,林鸿飞,等.基于支持向量机的生物医学文献蛋白质关系抽取[J].智能系统学报,2008,3(4):361.
　YANG Zhi-hao,HONG L i,L IN Hong-fei,et al.Extraction of information on prote in2prote in interaction from biomedical literatures using an SVM[J].CAAI Transactions on Intelligent Systems,2008,3():361.
[7]刘? 琚,乔建苹.基于学习的超分辨率重建技术[J].智能系统学报,2009,4(3):199.
　LIU Ju,QIAO Jian-ping.Learningbased superresolution reconstruction[J].CAAI Transactions on Intelligent Systems,2009,4():199.
[8]刘胜,李高云,江娜.SVM性能的免疫鱼群多目标优化研究[J].智能系统学报,2010,5(2):144.
　LIU Sheng,LI Gao-yun,JIANG Na.Multiobjective optimization of an immune fish swarm algorithm to improve support vector machine performance[J].CAAI Transactions on Intelligent Systems,2010,5():144.
[9]杨振兴,刘久富,孙琳.不变量的程序潜在错误预测[J].智能系统学报,2010,5(4):327.
　YANG Zhen-xing,LIU Jiu-fu,SUN Lin.Using invariants to predict the potential for errors in programs[J].CAAI Transactions on Intelligent Systems,2010,5():327.
[10]何世钊,杨宣访,陈晓娟.支持向量机与BP网络在火灾图像探测上的比较[J].智能系统学报,2011,6(4):339.
　HE Shizhao,YANG Xuanfang,CHEN Xiaojuan.Comparisons between a support vector machine and BP neural network for video image fire detection[J].CAAI Transactions on Intelligent Systems,2011,6():339.

备注/Memo

收稿日期： 2010-10-08.
基金项目：教育部科技发展中心网络时代的科技论文快速共享研究项目(20090043110010);吉林省科技规划资助项目(20090503)；吉林省教育厅“十一五”科研规划资助项目(2009587).
通信作者：孙铁利.E-mail:suntl@nenu.edu.cn.
作者简介：古丽娜孜,女，1972年出生，讲师，主要研究方向为数据挖掘、文本分类等，发表学术论文10余篇.
孙铁利，男，1956年出生，教授，博士生导师，伊犁师范学院兼职教授.主要研究方向为智能用户接口、智能信息挖掘.近年来承担国家级、省部级科研项目8项.发表学术论文100余篇，出版专著及教材10余部.
伊力亚尔，男，1978年出生，讲师，主要研究方向为计算机应用、自然语言信息处理，发表学术论文10余篇.

更新日期/Last Update: 2011-07-23

一种基于主动学习支持向量机哈萨克文文本分类方法 PDF下载HTML

备注/Memo

一种基于主动学习支持向量机哈萨克文文本分类方法

PDF下载 HTML