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» Learning the Structure of Linear Latent Variable Models
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CVPR
2000
IEEE
14 years 9 months ago
Impact of Dynamic Model Learning on Classification of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and analysis of figure motion has employed eith...
Vladimir Pavlovic, James M. Rehg
ECCV
2002
Springer
14 years 9 months ago
Audio-Video Sensor Fusion with Probabilistic Graphical Models
Abstract. We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it...
Matthew J. Beal, Hagai Attias, Nebojsa Jojic
SAT
2010
Springer
136views Hardware» more  SAT 2010»
13 years 11 months ago
A Non-prenex, Non-clausal QBF Solver with Game-State Learning
Abstract. We describe a DPLL-based solver for the problem of quantified boolean formulas (QBF) in non-prenex, non-CNF form. We make two contributions. First, we reformulate clause...
William Klieber, Samir Sapra, Sicun Gao, Edmund M....
ANOR
2011
175views more  ANOR 2011»
13 years 2 months ago
Integrated exact, hybrid and metaheuristic learning methods for confidentiality protection
A vital task facing government agencies and commercial organizations that report data is to represent the data in a meaningful way and simultaneously to protect the confidentialit...
Fred Glover, Lawrence H. Cox, Rahul Patil, James P...
AAAI
2008
13 years 10 months ago
Learning and Inference with Constraints
Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...