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GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
16 years 9 days ago
GAMM: genetic algorithms with meta-models for vision
Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an ex...
Greg Lee, Vadim Bulitko
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...
ATAL
2008
Springer
15 years 8 months ago
Sequential decision making in repeated coalition formation under uncertainty
The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learnin...
Georgios Chalkiadakis, Craig Boutilier
GECCO
2008
Springer
128views Optimization» more  GECCO 2008»
15 years 7 months ago
Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments
This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments con...
Gilles Énée, Mathias Péroumal...
ML
2006
ACM
110views Machine Learning» more  ML 2006»
15 years 6 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez