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TSP
2010
14 years 8 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
IUI
2010
ACM
15 years 8 months ago
From documents to tasks: deriving user tasks from document usage patterns
A typical knowledge worker is involved in multiple tasks and switches frequently between them every work day. These frequent switches become expensive because each task switch req...
Oliver Brdiczka
JMLR
2010
149views more  JMLR 2010»
14 years 8 months ago
Learning Bayesian Network Structure using LP Relaxations
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference pr...
Tommi Jaakkola, David Sontag, Amir Globerson, Mari...
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ALT
2001
Springer
15 years 10 months ago
Learning by Switching Type of Information
The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the le...
Sanjay Jain, Frank Stephan
ICML
2005
IEEE
16 years 2 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers