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ICDM
2009
IEEE
152views Data Mining» more  ICDM 2009»
15 years 2 months ago
A Sparsification Approach for Temporal Graphical Model Decomposition
Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct te...
Ning Ruan, Ruoming Jin, Victor E. Lee, Kun Huang
PAMI
2011
14 years 11 months ago
Greedy Learning of Binary Latent Trees
—Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are the latent ...
Stefan Harmeling, Christopher K. I. Williams
APBC
2004
132views Bioinformatics» more  APBC 2004»
15 years 5 months ago
A Novel Feature Selection Method to Improve Classification of Gene Expression Data
This paper introduces a novel method for minimum number of gene (feature) selection for a classification problem based on gene expression data with an objective function to maximi...
Liang Goh, Qun Song, Nikola K. Kasabov
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 8 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
ICAI
2010
15 years 2 months ago
Exact Loopy Belief Propagation on Euler Graphs
Belief propagation is widely used in inference of graphical models. It yields exact solutions when the underlying graph is singly connected. When the graph contains loops, double-c...
Chen-Hsiang Yeang