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IJHIS
2006
94views more  IJHIS 2006»
15 years 4 months ago
A new fine-grained evolutionary algorithm based on cellular learning automata
In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary ...
Reza Rastegar, Mohammad Reza Meybodi, Arash Hariri
162
Voted
UAI
2004
15 years 6 months ago
Factored Latent Analysis for far-field Tracking Data
This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent cl...
Chris Stauffer
125
Voted
UAI
2001
15 years 6 months ago
Learning the Dimensionality of Hidden Variables
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
Gal Elidan, Nir Friedman
167
Voted
NMR
2004
Springer
15 years 10 months ago
Probabilistic reasoning in dynamic multiagent systems
Probabilistic reasoning with multiply sectioned Bayesian networks (MSBNs) has been successfully applied in static domains under the cooperative multiagent paradigm. Probabilistic ...
Xiangdong An, Yang Xiang, Nick Cercone
AAAI
2011
14 years 4 months ago
Incorporating Boosted Regression Trees into Ecological Latent Variable Models
Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit model...
Rebecca A. Hutchinson, Li-Ping Liu, Thomas G. Diet...