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ICML
2009
IEEE
14 years 10 months ago
Model-free reinforcement learning as mixture learning
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
Nikos Vlassis, Marc Toussaint
DICTA
2009
13 years 11 months ago
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data
In many applied problems in the context of pattern recognition, the data often involve highly asymmetric observations. Normal mixture models tend to overfit when additional compone...
Kui Wang, Shu-Kay Ng, Geoffrey J. McLachlan
KI
2009
Springer
14 years 4 months ago
Maximum a Posteriori Estimation of Dynamically Changing Distributions
This paper presents a sequential state estimation method with arbitrary probabilistic models expressing the system’s belief. Probabilistic models can be estimated by Maximum a po...
Michael Volkhardt, Sören Kalesse, Steffen M&u...
ACL
2009
13 years 7 months ago
Variational Inference for Grammar Induction with Prior Knowledge
Variational EM has become a popular technique in probabilistic NLP with hidden variables. Commonly, for computational tractability, we make strong independence assumptions, such a...
Shay B. Cohen, Noah A. Smith
EMS
2009
IEEE
14 years 4 months ago
Modelling Periodic Data Dissemination in Wireless Sensor Networks
—Epidemic-based communications, or “gossiping”, provides a robust and scalable method for maintaining a knowledge base in a sensor network faced with an unpredictable network...
Graham Williamson, Davide Cellai, Simon A. Dobson,...