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

基于自劈分合并竞争学习的HMMs聚类方法 被引量：1

HMMs clustering approach based on self-splitting and merging competitive learning
在线阅读 下载PDF
导出
摘要 隐马尔可夫模型(Hidden Markov Model)是一种双随机过程,被广泛地应用于信号处理和模式识别中。但当将其应用于聚类时,对隐马尔可夫模型的训练,即参数估计,是一个非常重要的问题,训练方法的优劣将对整个应用效果产生重要的影响。论文针对这一问题,将自劈分合并竞争学习运用于HMMs。实验结果证实了提出方法的有效性。 Hidden Markov Model has been widely used as a valuable stochastic model.A novel clustering method based on both self-splitting and merging competitive learning and HMMs is proposed to improve the quality of the clustering.Experimental resuits show that our algorithm is very effective for such gene expression datasets.
作者 卢鸣 王士同
机构地区 江南大学信息工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第5期172-174,177,共4页 Computer Engineering and Applications
关键词 基因表达数据分析 自劈分合并竞争学习 隐马尔可夫模型 Gene Expression Data Analysis Self-Splitting and Merging Competitive Learning（SSMCL） Hidden Markov Model （HMM）
分类号 TP183 [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8

  • 1Bilmes J A.A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models.Technical Report TR-97-021[R].lnternational Computer Science Institute,Berkeley,CA,1998.
  • 2Rabiner L R.A tutorial on hidden markov models and selecter applications in speech recognition[J].Proceedings of The IEEE,1989,77 (2):257-286.
  • 3Schliep A,Steinhoff C,Schonhuth R.A inference of groups in Gene expression Time-courses using mixtures of HMM[J].Bioinformatics,2005,20 (10):2544-2545.
  • 4Wu Shuan-hu,Yan Hong,Yang Men-gsu,et al.Cluster analysis of gene expression data based on self-splitting and merging competitive learning[J].IEEE Transactions on Information Technology In Biomedicine,20004,8 (1):5-14.
  • 5Chung F L,Wang Shi-tong,et al.Clustering analysis of gene expression data based on semi-supervised clustering algorithm[J].Soft computing,2006 (11):981-994.
  • 6邓赵红,王士同,吴锡生,胡德文.鲁棒的极大熵聚类算法RMEC及其例外点标识[J].中国工程科学,2004,6(9):38-45. 被引量：12
  • 7Juang B H,Rabiner L R.The segmental K2Means algorithm for estimating the parameters of hidden Markov models[J].IEEE Trans on Accoustic,Speech,and Signal Processing,1990,38(9):1639-1641.
  • 8Rabiner L R,Juang B H.An introduction to hidden Markov models[J].IEEE Accoustic,Speech,and Signal Processing Magazine,1986,3(1):4-16.

二级参考文献13

  • 1Rose K, Gurewtiz E, Fox G. A deterministic annealing approach toclustering [J]. Pattern Recognition Letters, 1990, 11: 589～ 594
  • 2Keller A. Fuzzy clustering with outliers [A].NAFIPS00 [M]. 2000
  • 3Karayiannis N B. MECA: maximum entropy clustering algorithm [ A]. Proc on IEEE Int Conf on Fuzzy Syst [C]. Orlando, F L, 1994. 630～635
  • 4Li R P, Mukaidono M. A maximum entropy approach to fuzzy clustering [A]. Proc on IEEE Iht Conf Fuzzy Syst[C]. Yokohama, Japan, 1995. 2227～2232
  • 5Las M, Kandel A. Automated perceptions in data mining [A]. Proceedings of the Eighth InternationalConference on Fuzzy System [ C]. Seoul, Korea,1999. 190～ 197
  • 6Mendenhall W, Reinmuth J E, Beaver R J, Statistics for management and economics [ M]. Belmont, C A:Duxbury Press, 1993
  • 7Huber P J, Robust statistics [ M]. New York: Wiley,1981
  • 8Gill P E, Murray W. Wright M H, Practical Optimization [ M]. New York: Academic Press, 1981
  • 9Steve R G, Support vector machines classification and regression [R]. University of Southampton, 1998
  • 10Vapnik V, Statistical learning theory [ M]. New York: Wiley, 1998

共引文献11

同被引文献3

引证文献1

计算机工程与应用
2007年 第5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