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

半监督学习贝叶斯分类(英文) 被引量：1

Semisupervised Learning of Bayesian Classification
在线阅读 下载PDF
导出
摘要 分类器的学习采用半监督贝叶斯方法,使用EM算法求解最大似然估计,实验结果表明能够获得较好的结果。 To learn a classifier,a semisupervised Bayesian approach is adopted. An EM algorithm is derived to compute maximum likelihood estimate. Experimental results demonstrate appropriate accuracy.
作者 孔怡青 王士同
机构地区 江南大学信息学院
出处 《广西师范大学学报（自然科学版）》 CAS 北大核心 2006年第4期99-102,共4页 Journal of Guangxi Normal University:Natural Science Edition
基金 New Century Outstanding Young Scholar Grant (NCET-04-0496)
关键词 贝叶斯方法 半监督学习 EM算法 Bayesian approach semisupervised learning M
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献9

  • 1MILLER D J,UYAR H.A mixture of experts classifier with learning based on both labelled and unlabelled data[C]//MOZER M,JORDAN M,PETSCHE T.Proc Advances in Neural Information Processing Systems 9.Cambridge,MA:MIT Press,1997:571-577.
  • 2CASTELLI V,COVER T.On the exponential value of labeled samples[J].Pattern Recognition Letters,1995,16:105-111.
  • 3COHEN I,COZMAN F G,SEBE N,et al.Semisupervised learning of classifiers:theory,algorithms,and their application to human-computer interaction[J].IEEE Trans Pattern Analysis and Machine Intelligence,2004,26:1553-1567.
  • 4DEVROYE L,GYORFI L,LUGOSI G.A probabilistic theory of pattern recognition[M].New York:Springer Verlag,1996.
  • 5PARZEN E.On estimation of a probability density function and mode[J].Annals of Math Statistics,1962,33:1065.
  • 6IZENMAN A J.Recent developments in nonparametric density estimation[J].J Am Statistical Assoc,1991,86:205.
  • 7SILVERMAN B W.Kernel density estimation using the fast Fourier transform[J].Applied Statistics,1982,31:93.
  • 8McLACHLAN G,KRISHNAN T.The EM algorithm and extensions[M].New York:John Wiley & Sons,1997.
  • 9RIPLEY B.Pattern recognition and neural networks[M].Cambridge:Cambridge University Press,1996.

同被引文献7

  • 1JOACHIMS T. Transductive inference for text classification using support vector machines[C]//Proceedings of the Sixteenth International Conference on Machine Learning. San Fransisco :Morgan Kaufmann, 1999 : 200-209.
  • 2BELKIN M,MATVEEVA I,NIYOGI P. Regression and regularization on large graphs [C]//Proceedings of the 17th Annual Conference on Learning Theory. New York :ACM Press, 2004 : 185-192.
  • 3中国教育和科研计算机网紧急响应组.CCERT中文邮件样本集[EB/OL].北京:中国教育和科研计算机网紧急响应组,2005[2008-01-10].http://www.ccert.edu.cn/spam/sa/datasets.htm.
  • 4VAPNIK V. The nature of statistical learning theory[M]. New York :Springer, 1995 : 110-117.
  • 5CHAPELLE O ,SCHOLKOPF B,ZIEN A. Semi-supervised learning[M]. Cambridge ,MA :MIT Press, 2006 : 1-13.
  • 6BLUM A,MITCHELL T. Combining labeled and unlabeled data with co-training[C]//Proceedings of the llth Annual Conference on Computational Learning Theory. New York :ACM Press, 1998: 92-100.
  • 7DEMPSTER A P,LAIRD N M,RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J].Journal of the Royal Statistical Society,Series B,1977,39(1):1-38.

引证文献1

二级引证文献9

广西师范大学学报（自然科学版）
2006年 第4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