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» Backbones in Optimization and Approximation
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DAS
2006
Springer
15 years 10 months ago
Combining Multiple Classifiers for Faster Optical Character Recognition
Traditional approaches to combining classifiers attempt to improve classification accuracy at the cost of increased processing. They may be viewed as providing an accuracy-speed tr...
Kumar Chellapilla, Michael Shilman, Patrice Simard
ECML
2006
Springer
15 years 10 months ago
PAC-Learning of Markov Models with Hidden State
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...
Ricard Gavaldà, Philipp W. Keller, Joelle P...
ECML
2006
Springer
15 years 10 months ago
Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions
We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJ...
Sébastien Jodogne, Justus H. Piater
GECCO
2006
Springer
143views Optimization» more  GECCO 2006»
15 years 10 months ago
DECA: dimension extracting coevolutionary algorithm
Coevolution has often been based on averaged outcomes, resulting in unstable evaluation. Several theoretical approaches have used archives to provide stable evaluation. However, t...
Edwin D. de Jong, Anthony Bucci
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
15 years 10 months ago
Standard and averaging reinforcement learning in XCS
This paper investigates reinforcement learning (RL) in XCS. First, it formally shows that XCS implements a method of generalized RL based on linear approximators, in which the usu...
Pier Luca Lanzi, Daniele Loiacono