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» Learning Markov Network Structure with Decision Trees
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TNN
1998
123views more  TNN 1998»
13 years 8 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti
PKDD
2005
Springer
95views Data Mining» more  PKDD 2005»
14 years 2 months ago
Ensembles of Balanced Nested Dichotomies for Multi-class Problems
Abstract. A system of nested dichotomies is a hierarchical decomposition of a multi-class problem with c classes into c − 1 two-class problems and can be represented as a tree st...
Lin Dong, Eibe Frank, Stefan Kramer
FLAIRS
2004
13 years 10 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
AAAI
2006
13 years 10 months ago
Action Selection in Bayesian Reinforcement Learning
My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques b...
Tao Wang
GLOBECOM
2006
IEEE
14 years 2 months ago
Adaptive Learning of Transmission Control Policies for MIMO Fading Channels under Delay Constraint
— This paper addresses learning based adaptive resource allocation for wireless MIMO channels with Markovian fading. The problem is posed as Constrained Markov Decision Process w...
Dejan V. Djonin, Vikram Krishnamurthy