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» Learning Nonlinear Dynamical Systems Using an EM Algorithm
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TSMC
2008
229views more  TSMC 2008»
14 years 9 months ago
A Comprehensive Survey of Multiagent Reinforcement Learning
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many task...
Lucian Busoniu, Robert Babuska, Bart De Schutter
BMCBI
2006
239views more  BMCBI 2006»
14 years 9 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
ECCV
2004
Springer
15 years 11 months ago
A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks
This paper presents a biologically motivated model for low and mid-level vision tasks and its interpretation in computer vision terms. Initially we briefly present the biologically...
Iasonas Kokkinos, Rachid Deriche, Petros Maragos, ...
JAIR
2002
120views more  JAIR 2002»
14 years 9 months ago
Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap
Hidden Markov models hmms and partially observable Markov decision processes pomdps provide useful tools for modeling dynamical systems. They are particularly useful for represent...
Hagit Shatkay, Leslie Pack Kaelbling
PAMI
2007
186views more  PAMI 2007»
14 years 9 months ago
Value-Directed Human Behavior Analysis from Video Using Partially Observable Markov Decision Processes
—This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the co...
Jesse Hoey, James J. Little