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» Learning Causal Structure from Overlapping Variable Sets
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KDD
2007
ACM
209views Data Mining» more  KDD 2007»
14 years 8 months ago
Temporal causal modeling with graphical granger methods
The need for mining causality, beyond mere statistical correlations, for real world problems has been recognized widely. Many of these applications naturally involve temporal data...
Andrew Arnold, Yan Liu, Naoki Abe
ICML
2009
IEEE
14 years 8 months ago
Structure learning of Bayesian networks using constraints
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
VMCAI
2005
Springer
14 years 1 months ago
Minimizing Counterexample with Unit Core Extraction and Incremental SAT
Abstract. It is a hotly researching topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. K Ravi proposes a two-stages counterexample min...
ShengYu Shen, Ying Qin, Sikun Li
DMIN
2006
125views Data Mining» more  DMIN 2006»
13 years 9 months ago
Privacy-Preserving Bayesian Network Learning From Heterogeneous Distributed Data
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
Jianjie Ma, Krishnamoorthy Sivakumar
CVPR
2012
IEEE
11 years 10 months ago
Learning latent temporal structure for complex event detection
In this paper, we tackle the problem of understanding the temporal structure of complex events in highly varying videos obtained from the Internet. Towards this goal, we utilize a...
Kevin Tang, Fei-Fei Li, Daphne Koller