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一种新的半监督支持向量机 被引量:6

A New Semi-supervised Support Vector Machine
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摘要 在模式识别中,采取支持向量机对有类别标签样本分类是非常有效的,但在实际应用中,对样本进行标记并不是一件容易的工作.通过综合利用有类别标签和无类别标签样本信息构造目标函数和约束条件,借助二次规划模型提出了一种新的半监督支持向量机,从而提高了仅依靠有类别标签样本支持向量机的分类准确率. Support vector machines(SVMs) were effective in various areas,especially in pattern classification.A quadratic program model of classifying unlabeled and labeled data was presented by constructing a new objective function and constraints.Based on this model,a new algorithm of semi-supervised learning was presented to improve the accuracy of SVMs when a rarely labeled data appeared.
出处 《郑州大学学报(理学版)》 CAS 北大核心 2012年第3期66-68,共3页 Journal of Zhengzhou University:Natural Science Edition
基金 河南省基础与前沿技术研究项目 编号122300410229 河南省教育厅自然科学基金资助项目 编号12B110005
关键词 支持向量机 二次规划 无标签 半监督 SVM quadratic programming unlabeled data semi-supervised learning
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参考文献9

  • 1Vapnik V. The Nature of Statistical Learning Theory[ M]. New York: Springer-Verlag, 1995.
  • 2Blum A, Mitchell T. Combining labeled and unlabeled data with cotraining [ C ]//Proceedings of the 11 th Annual Conference on Computational Learning Theory. Madison, 1998 : 92 - 100.
  • 3Bennett K P, Demiriz A. Semi-supervised Support Vector Machines [ C]//Advances in Neural Information Proceeing Systems 11. Cambridge, 1998 : 368 - 374.
  • 4Fung G, Mangasarian O L. Semi-supervised support vector machines for unlabeled data classification[ J]. Optimization Methods and Software,2001,15 : 29 - 44.
  • 5Wu Tao, Zhao Hanqing. Classifying unlabeled data with SVMs [ J ]. Advances in InteUigent and Soft Computing, 2006,34: 695 - 702.
  • 6门昌骞,王文剑.一种基于多学习器标记的半监督SVM学习方法[J].广西师范大学学报(自然科学版),2008,26(1):186-189. 被引量:9
  • 7朱美琳,杨佩.半监督支持向量机的多分类学习算法[J].郑州大学学报(理学版),2008,40(4):35-38. 被引量:4
  • 8Hsu C W, Lin C J. A simple decomposition method for support vector machines [ J ]. Machine Learning,2002,46 (1/2/3) :291 -314.
  • 9Blake C L, Merz C J. UCI Repository of machine learning databases[ EB/OL]. [2011 -01 - 11 ] . http://www, ics. uci. edu/ -mlearn/databases/.

二级参考文献17

  • 1孔怡青,王士同.半监督学习贝叶斯分类(英文)[J].广西师范大学学报(自然科学版),2006,24(4):99-102. 被引量:1
  • 2Vapnik V. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995.
  • 3Cristianini N,John S T. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M]. Cambridge University Press, English, 2000.
  • 4Zhu Xiaojin. Semi-supervised learning literature survey[EB/OL]. [2008-07-19]. http://pages. cs. wisc. edu/- jerryzhu/ pub/ssl.survey. pdf.
  • 5Bennett K P,Demiriz A. Semi-supervised Support Vector Machines[J]. Advances in Neural Information Processing Systems, 1998 (11) : 368-374.
  • 6Krebel U. Pairwise Classification and Support Vector Machines[M]. Advances in Kernel Methods-Support Vector Learning. MA: MIT Press, 1999:255-268.
  • 7Schwenker F,Palm G. Tree-structured Support Vector Machines for multi-class pattern recognition[C]//Proceedings of the 2th International Workshop on Multiple Classifier Systems. London: Springer-Verlag, 2001,2096 : 409-417.
  • 8Bredensteiner E J,Bennett K P. Multicategory classification by Support Vector Machine[J]. Computational Optimization and Applications, 1999,12(1/2/3): 53-79.
  • 9Tax D M J. One-class classification [D]. Nijmegen:University of Nijmegen,2001.
  • 10Zhu Meilin,Wang Yue, Chen Shifu, et al. Sphere-structured Support Vector Machines for multi-class pattern recognition [C]///The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Berlin : Springer Berlin Heidelbery, 2003,2639 : 589-593.

共引文献11

同被引文献34

  • 1平源.基于支持向量机的聚类及文本分类研究[D].北京:北京邮电大学,2012.
  • 2Zou H, Hastie T. Regularization and variable selection via the elastic net [J]. J Royal Statistical Society B, 2005, 67(2) :301 - 320.
  • 3Wang L, Zhu J, Zou H. Hybrid huberized support vector machines for microarray classification and gene selection [ J ]. Bioin- formatics, 2008, 24(3) : 412 - 419.
  • 4Wang L, Zhu J, Zou H. The doubly reg~arizrd support vector machine [J]. Statistica Sinica, 2006, 16(2) : 589 -615.
  • 5Guyon I, Weston J, Bamhill S, et al. Gene selection for cancer classification using support vector machines [J].Machine Learning,2002,46( 1 ) :389 -422.
  • 6Li J, Jia Y, Zhao Z. Partly adaptive elastic net and its application to microarray classfication [ J]. Neural Comput and Applic, 2013,22(6) :1193 - 1200.
  • 7Hastie T, Rosset S, Tibshirani B, et al. The entire regularization path for the support vector machine [J]. Journal of Machine Learning Research,2004,5 : 1391 - 1415.
  • 8Van der Stede W A. The relationship between two consequences of budgetary controls: budgetary slacks creation and manage- ment short-term orientation [ J ]. Accounting, Organizations and Society, 2002, 17 (3) : 609 - 622.
  • 9Jensen M C. Paying people to lie : the truth about the budgeting process [ J ]. European Financial Management, 2003 (9) : 379 -406.
  • 10RichardScoRW,DavisGeraldF.组织理论一理性、自然与开放系统的视角[M].北京:中国人民大学出版社,2011:172-174.

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