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FLAIRS
2004
13 years 8 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
JMLR
2002
89views more  JMLR 2002»
13 years 6 months ago
The Set Covering Machine
We extend the classical algorithms of Valiant and Haussler for learning compact conjunctions and disjunctions of Boolean attributes to allow features that are constructed from the...
Mario Marchand, John Shawe-Taylor
AAAI
2008
13 years 9 months ago
Active Learning for Pipeline Models
For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, c...
Dan Roth, Kevin Small
EMNLP
2006
13 years 8 months ago
Automatic Construction of Predicate-argument Structure Patterns for Biomedical Information Extraction
This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training...
Akane Yakushiji, Yusuke Miyao, Tomoko Ohta, Yuka T...
ECML
2004
Springer
14 years 3 days ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst