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» Graphical Models: Statistical inference vs. determination
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BMCBI
2011
12 years 11 months ago
A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-w
Background: Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1...
Raphael Mourad, Christine Sinoquet, Philippe Leray
AI
2002
Springer
13 years 7 months ago
The size distribution for Markov equivalence classes of acyclic digraph models
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
Steven B. Gillispie, Michael D. Perlman
CVPR
2007
IEEE
14 years 9 months ago
Learning the Compositional Nature of Visual Objects
The compositional nature of visual objects significantly limits their representation complexity and renders learning of structured object models tractable. Adopting this modeling ...
Björn Ommer, Joachim M. Buhmann
EMNLP
2008
13 years 8 months ago
One-Class Clustering in the Text Domain
Having seen a news title "Alba denies wedding reports", how do we infer that it is primarily about Jessica Alba, rather than about weddings or reports? We probably reali...
Ron Bekkerman, Koby Crammer
SIGMOD
1998
ACM
180views Database» more  SIGMOD 1998»
13 years 11 months ago
Integration of Heterogeneous Databases Without Common Domains Using Queries Based on Textual Similarity
Most databases contain “name constants” like course numbers, personal names, and place names that correspond to entities in the real world. Previous work in integration of het...
William W. Cohen