期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于SVM的一种新的分类器设计方法 被引量：5

Novel Algorithm for Designing Classifier Based on SVM
下载PDF
导出
摘要 根据“半监督学习”方法,利用已经标注好的训练样本和无标注的训练样本一起训练分类器。在标准SVM分类器训练方法中融入这种思想,给分类面附近加入混合数据,提出了一种新的基于SVM的分类器设计方法,并将这种方法应用于小样本数据的分类问题中。实验表明,新的基于SVM的分类器与传统SVM相比较,在分类准确率上有很大提高,同时偏差有所降低。 According to ＂semisupervised learning＂, both labeled and unlabeled data are used to train classifier. Combining this idea with standard SVM classifier and adding a mixed data sets near the interface, a new SVM learning algorithm is proposed for classification of small data sets. Compared with the standard SVM algorithm, the experiments show our new classifier can both improve the classification accuracy and reduce the bias.
作者 刘晶 郭雷 聂晶鑫
机构地区 西北工业大学自动化学院
出处 《计算机应用研究》 CSCD 北大核心 2006年第7期181-182,185,共3页 Application Research of Computers
基金 国家自然科学基金资助项目(60175001)
关键词 小样本数据 SVM分类器 分类准确率 半监督学习 Small Data Sets SVM Classifiers Classification Accuracy Semisupervised Learning
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献7

  • 1Andrew R Webb, Statistical Pattern Recognition(2nd edition)[M].Publishing House of Electronics Industry, 2004.5-6,106-111.
  • 2Ira Cohen, Fabio G Cozman, Nieu Sebe, et al. Semisupervised Learning of Classifiers : Theory, Algorithms, and Their Application to Human-Computer Interaction [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(12):1553-1567.
  • 3Edgar E Osuna, Robert Freund, Federico Girosi, Support Vector Machines:Training and Applications [R]. Massachusetts Institute of Technology, 1997.28-30.
  • 4付岩,王耀威,王伟强,高文.SVM用于基于内容的自然图像分类和检索[J].计算机学报,2003,26(10):1261-1265. 被引量：54
  • 5Chris J C Burges, Beruhard Scholkopf. Improving the Accuracy and Speed of Support Vector Machines[C]. Advances in Neural Information Processing Systems, MIT Press, 1997, 375-382.
  • 6Thorsten Joachims. Learning to Classify Text Using Support Vector Machines Method, Theory, and Algorithms [M]. Kluwer Academic Publishers, 2002. 140-160.
  • 7Platt J C, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machine [M]. Cambridge, MA: MIT Press,1999. 185-206.

二级参考文献15

  • 1Burkhardt H, Siggelkow S. Invariant features for discriminating between equivalence classes. In:Nonlinear Model-based Image Video Processing and Analysis. NY: John Wiley and Sons,2000.
  • 2Scholkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond.Cambridge, Mass: MIT Press, 2002.
  • 3Vapnik V N. The Nature of Statistical Learning Theory. NewYork: Springer-Verlag, 2000.
  • 4Scholkopf B, Burges C J C, Smola A J. Advances in Kernel Methods—Support Vector Learning. Cambridge, MA: MIT Press, 1999.
  • 5Smeulders A, Worring M et al. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(12) : 1349～ 1380.
  • 6Flickner M et al. Query by image and video content: The QBIC system. IEEE Computer, 1995,28(9) : 23 ～32.
  • 7Bach J R, Fuller C, Gupta Aet al. Virage image search engine: an open framework for image management. SPIE Storage and Retrieval of Image and Video DataBases, 1996,4:76 ～87.
  • 8Smith J, Chang S F. VisualSEEK: A fully automated contentbased image query system. In: Proceedings of the 4th ACM Multimedia Conference,Boston MA, USA, 1996.87～98.
  • 9Vailaya A, Figueiredo M, Jain A, Zhang H-J. A Bayesian framework for semantic classification of outdoor vacation images. In: Proceedings of SPIE:Storage and Retrieval for Image and Video Databases VII, San Jose, CA, USA, 1999,3656:415～426.
  • 10Lipson P, Grimson E, Sinha P. Configuration based scene classification and image indexing. In: Proceedings of the 16th IEEE Conference on Computer Vision and Pattern Recognition,Puerto Rico, 1997. 1007～1013.

共引文献53

同被引文献32

  • 1胡永刚,吴翊,王洪志,卜江.高维数据降维的DCT变换[J].计算机工程与应用,2006,42(32):21-23. 被引量：9
  • 2国家技术监督局 中华人民共和国卫生部.中华人民共和国国家标准：中医临床诊疗术语证候部分[M].北京:中国标准出版社,1997.126.
  • 3Rocchio J. Relevant feedback in information retrieval[ M]//In Salton G. The smart retrieval system - experiments in automatic document processing. Englewood Cliffs, NJ: [s. n. ], 1971.
  • 4MeCaUum A, Nigam K. A comparison of event models for naive Bayes text classification [ C]//AAAI - 98 Workshop on Learning for Text Categorization. [s. l. ] :AAAI Press, 1998.
  • 5Guyon I, Boser B, Vapnik V. Automatic capacity tuning of very large Vcdimension classifiers[J ]. Advances in Neural Information Processing Systems, 1993(5):147- 155.
  • 6Igam K,McCallum A,Thrun S,et al. Learning to classify text from labeled and unlabeled documents [ C]//In: Mostow J, Madison C R. Proceedings d the 15th National Conference on Artificial Intelligence. Wisconsin: AAAI Press, 1998:792- 799.
  • 7Engelbreeht A P, Cloete I. Incremental Learning Using Sensitivity Analysis[C]//Neural Networks, 1999. IJCNN apos; 99. International Joint Conferenoe. [s. l. ] : IEEE Press, 1999: 1350 - 1355.
  • 8Thompson C A,Califf M E,Mooney R J. Active Learning for Natural Language Parsing and Information Extraction[C]// In:Proceedings of the sixteenth International Machine Learning Conference. Slovenia: [ s. n. ], 1999.
  • 9Cohn D A, Ghahramani Z, Jordan M I. Active learning with statistical models [ J ]. J. of Artidal Intelligence Research, 1996,4:129 - 145.
  • 10Liere R,Tadepalli P. Active learning with oommittees for text categofizeation[ C]//In Proceedings of the Fourteenth National Conference on Articial Intelligence. Providence, RI: [ s. n. ], 1997:591 - 596.

引证文献5

二级引证文献30

计算机应用研究
2006年 第7期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部