期刊文献+

贝叶斯网络适应性学习 被引量:3

Adaptability Learning for Bayesian Network
下载PDF
导出
摘要 在现实中,随着对领域问题认识的深入,往往需要对贝叶斯网络进行调整,以使贝叶斯网络模型能够更好地反映实际问题.但调整后的贝叶斯网络中一些新参数需要根据原有贝叶斯网络来确定,目前缺乏对新参数学习方法的研究.本文基于专家知识调整贝叶斯网络结构,将原贝叶斯网络和新贝叶斯网络相结合,通过推理进行新参数的迭代学习,可实现贝叶斯网络的适应性学习. With further understanding to the domanial problems, it is often necessary to regulate the Bayesian network to meet the demand. But in the regulated Bayesian network the new parameters need to be computed according to the old Bayesian network. At present, however, lack the research of the methods of learning new parameters. In this paper, Bayesian network structure is regulated based on experts knowledge. The old Bayesian network is combined with the new one to ascertain the new parameters through reasoning. The adaptability learning of Bayesian network can be realized by above method.
出处 《小型微型计算机系统》 CSCD 北大核心 2009年第4期706-709,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(60675036)资助
关键词 贝叶斯网络 适应性学习 马尔科夫毯预测 结构学习 参数学习 bayesian networks adaptability learning markov blanket prediction structure learning parameter learning
  • 相关文献

参考文献10

二级参考文献38

  • 1Buntine W.Theory refinement on Bayesian networks[A].Ambrosio B D,Smets P Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence[C].Los Angeles:Morgan Kaufmann,1991.52-60. 被引量:1
  • 2Lam W,Bacchus F.Using new data to refine a Bayesian network[A].Ramon López de Mántaras,David Poole Proceedings of the Tenth Annual Conference on Uncertainty in Artificial Intelligence[C].San Mateo:Morgan Kaufmann,1994.383-390. 被引量:1
  • 3Friedman N,Goldszmidt M.Sequential update of Bayesian network structure[A].Dan Geiger,Prakash P.Shenoy.Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence[C].Morgan Kaufmann,1997.165-174. 被引量:1
  • 4J R Alcobé.An incremental algorithm for tree-shaped bayesian network lear ning[A].Frank van Harmelen Proceedings of the 15th European Conference on Artificial Intelligence[C].Lyon:IOS Press,2002.350-354. 被引量:1
  • 5J R Alcobé.Incremental hill-climbing search applied to bayesian network structure lear ning[A].Proceedings of the 15th European Conference on Machine Lear ning[C],Pisa,Italy,2004. 被引量:1
  • 6Friedman N.Lear ning belief networks in the presence of missing values and hidden variables[A].Proceedings of the 14th Inter national Conference on Machine Lear ning[C].Madison,1997.452-459. 被引量:1
  • 7Myers J W,Laskey K B,DeJong K A.Lear ning bayesian networks from incomplete data using evolutionary algorithms[A].Banzhaf W,Daida J,Eiben AE et al.Proceedings of the Genetic and Evolutionary Computation Conference[C].San Francisco:Morgan Kaufmann,1999.458-465. 被引量:1
  • 8F Bromberg,B Patterson,S Yaramakala.Mining Bayesian Networks from Streamed Data[Z].CS 561 Final Report,Spring 2003. 被引量:1
  • 9Beinlich I,Suermondt G,Chavez R,et al.The ALARM monitoring system:A case study with two probabilistic inference techniques for belief networks[A].Proceedings of the Second European Conference on Artificial Intelligence in Medicine[C].Berlin:Springer-Verlag,1989.247-256. 被引量:1
  • 10Nir. Friedman. The Bayesian structural EM algorithm. The 14th Conf. Uncertainty in artificial Intelligence, San Francisco, 1998. 被引量:1

共引文献79

同被引文献39

  • 1戴劲松,白英彩.基于贝叶斯理论的垃圾邮件过滤技术[J].计算机应用与软件,2006,23(1):110-111. 被引量:16
  • 2史建国,高晓光,李相民.离散模糊动态贝叶斯网络用于无人作战飞机目标识别[J].西北工业大学学报,2006,24(1):45-49. 被引量:8
  • 3Song L, Kolar M, Xing E. Time varying dynamic Bayesian net works[C]// Proc. of the 23rd Neural Information Processing Systems ,2005 : 1732 - 1740. 被引量:1
  • 4Campos C P, Zeng Z, Ji Q. Structure learning of Bayesian net works using constraints [C]// Proc. of the 26th Annual International Conference on Machine Learning ,2009 : 113 - 120. 被引量:1
  • 5Chen H, Gao X. Forwards-backwards information repairing algorithm and appliance on discrete dynamic Bayesian networks[C]//Proc, of the International Comference on Intelligent Human-Machine Systems and Cybervtetics, 2C09 : 76 - 80. 被引量:1
  • 6Dojer N, Oambin A, Mizera A, et al. Applying dynamic Bayes Jan networks to perturbed gene expression data[J].BMCBioin formatics, 2006,7 ( 1 ) : 249. 被引量:1
  • 7Saenko K, Livescu K, Glass J, feature based models for visual et al. Multistream articulatory speech recognition [J].IEEE Trans. on Pattern Analysis and Machine Intelligence, 2009,31 (9) :1700 - 1707. 被引量:1
  • 8Hearty P, Fcnton N, Marquez D, et al. Predicting project velocity in XP using a learning dynamic Bayesian network model[J]. IEEE Trans onSoftware Enfineering,2009,35(1) :124 -137. 被引量:1
  • 9Rajapakse J C, Wang Y, Zheng X, et al. Probabilistie frame- work for br;dn connectivity from functional MR images[J]. IEEE Trans. on Medical Imagine ,2008,27(6):825 - 833. 被引量:1
  • 10Jaeger M. Pa-ameter learning for relational Bayesian networks [C]// Proc. of the 24th International Conference on Machine Learning, 2007:369 - 376. 被引量:1

引证文献3

二级引证文献15

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