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ICML
2003
IEEE
15 years 10 months ago
Learning Mixture Models with the Latent Maximum Entropy Principle
We present a new approach to estimating mixture models based on a new inference principle we have proposed: the latent maximum entropy principle (LME). LME is different both from ...
Shaojun Wang, Dale Schuurmans, Fuchun Peng, Yunxin...
ADHOCNETS
2010
Springer
14 years 6 months ago
DCLA: A Duty-Cycle Learning Algorithm for IEEE 802.15.4 Beacon-Enabled WSNs
The current specification for IEEE 802.15.4 beacon-enabled networks does not define how active and sleep schedules should be configured in order to achieve the optimal network perf...
Rodolfo de Paz Alberola, Dirk Pesch
NIPS
2007
14 years 11 months ago
Stable Dual Dynamic Programming
Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions i...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
JAIR
2011
144views more  JAIR 2011»
14 years 4 months ago
Non-Deterministic Policies in Markovian Decision Processes
Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making proble...
Mahdi Milani Fard, Joelle Pineau
NIPS
1998
14 years 11 months ago
Call-Based Fraud Detection in Mobile Communication Networks Using a Hierarchical Regime-Switching Model
Fraud causes substantial losses to telecommunication carriers. Detection systems which automatically detect illegal use of the network can be used to alleviate the problem. Previo...
Jaakko Hollmén, Volker Tresp