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IJUFKS
2000
111views more  IJUFKS 2000»
13 years 8 months ago
A Factorized Representation of Independence of Causal Influence and Lazy Propagation
Theefficiency of algorithmsfor probabilistic inference in Bayesian networks can be improvedby exploiting independenceof causal influence. Thefactorized representation of independe...
Anders L. Madsen, Bruce D'Ambrosio
ICMLA
2008
13 years 10 months ago
Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...
NECO
2002
104views more  NECO 2002»
13 years 8 months ago
An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
Harri Valpola, Juha Karhunen
ICML
2000
IEEE
14 years 9 months ago
Hierarchical Unsupervised Learning
We consider the problem of unsupervised classification of temporal sequences of facial expressions in video. This problem arises in the design of an adaptive visual agent, which m...
Shivakumar Vaithyanathan, Byron Dom
ICMLA
2009
13 years 6 months ago
Automatic Feature Selection for Model-Based Reinforcement Learning in Factored MDPs
Abstract--Feature selection is an important challenge in machine learning. Unfortunately, most methods for automating feature selection are designed for supervised learning tasks a...
Mark Kroon, Shimon Whiteson