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» Using Learning for Approximation in Stochastic Processes
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NLP
2000
15 years 6 months ago
Monte-Carlo Sampling for NP-Hard Maximization Problems in the Framework of Weighted Parsing
Abstract. The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in...
Jean-Cédric Chappelier, Martin Rajman
120
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CIKM
2009
Springer
15 years 9 months ago
Cross-language linking of news stories on the web using interlingual topic modelling
We have studied the problem of linking event information across different languages without the use of translation systems or dictionaries. The linking is based on interlingua in...
Wim De Smet, Marie-Francine Moens
RECOMB
2000
Springer
15 years 6 months ago
Using Bayesian networks to analyze expression data
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the c...
Nir Friedman, Michal Linial, Iftach Nachman, Dana ...
ICASSP
2008
IEEE
15 years 9 months ago
Maximum entropy relaxation for multiscale graphical model selection
We consider the problem of learning multiscale graphical models. Given a collection of variables along with covariance specifications for these variables, we introduce hidden var...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
CDC
2010
IEEE
136views Control Systems» more  CDC 2010»
14 years 9 months ago
Pathologies of temporal difference methods in approximate dynamic programming
Approximate policy iteration methods based on temporal differences are popular in practice, and have been tested extensively, dating to the early nineties, but the associated conve...
Dimitri P. Bertsekas