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» Structured Learning with Approximate Inference
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BMCBI
2010
229views more  BMCBI 2010»
14 years 9 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
NIPS
2000
14 years 11 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
IPL
2008
172views more  IPL 2008»
14 years 9 months ago
Approximation algorithms for restricted Bayesian network structures
Bayesian Network structures with a maximum in-degree of k can be approximated with respect to a positive scoring metric up to an factor of 1/k. Key words: approximation algorithm,...
Valentin Ziegler
JUCS
2008
100views more  JUCS 2008»
14 years 9 months ago
Stacked Dependency Networks for Layout Document Structuring
: We address the problems of structuring and annotation of layout-oriented documents. We model the annotation problems as the collective classification on graph-like structures wit...
Boris Chidlovskii, Loïc Lecerf
UAI
1998
14 years 11 months ago
Tractable Inference for Complex Stochastic Processes
The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system,...
Xavier Boyen, Daphne Koller