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	The role of operation granularity in search-based learning of latent tree models
	Tao Chen; Nevin L. Zhang; Yi Wang
	2011
会议名称	2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010
会议地点	Tokyo, Japan
英文摘要	Latent tree (LT) models are a special class of Bayesian networks that can be used for cluster analysis, latent structure discovery and density estimation. A number of search-based algorithms for learning LT models have been developed. In particular, the HSHC algorithm by [1] and the EAST algorithm by [2] are able to deal with data sets with dozens to around 100 variables. Both HSHC and EAST aim at finding the LT model with the highest BIC score. However, they use another criterion called the cost-effectiveness principle when selecting among some of the candidate models during search. In this paper, we investigate whether and why this is necessary.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3671]
专题	深圳先进技术研究院_其他
作者单位	2011
推荐引用方式 GB/T 7714	Tao Chen,Nevin L. Zhang,Yi Wang. The role of operation granularity in search-based learning of latent tree models[C]. 见:2nd JSAI International Symposia on Artificial Intelligence, JSAI-isAI 2010. Tokyo, Japan.

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