Floating search methods in feature selection
Pattern Recognition Letters
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Feature Subset Selection Using Genetic Algorithms for Handwritten Digit Recognition
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
Modeling Object Recognition as a Markov Decision Process
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
GAMM: genetic algorithms with meta-models for vision
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Machine learning for adaptive image interpretation
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
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Action set selection in Markov Decision Processes (MDPs) is an area of research that has received little attention. On the other hand, the set of actions available to an MDP agent can have a significant impact on the ability of the agent to gain optimal rewards. Last year at GECCO'05, the first automated action set selection tool powered by genetic algorithms was presented. The demonstration of its capabilities, though intriguing, was limited to a single domain. In this paper, we apply the tool to a more challenging problem of oil sand image interpretation. In the new experiments, genetic algorithms evolved a compact high-performance set of image processing operators, decreasing interpretation time by 98% while improving image interpretation accuracy by 55%. These results exceed the original performance and suggest certain cross-domain portability of the approach.