Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering novel fighter combat maneuvers: simulating test pilot creativity
Creative evolutionary systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Learning Classifier Systems, From Foundations to Applications
Learning Classifier Systems, From Foundations to Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fused, multi-spectral automatic target recognition with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
XCSF for prediction on emotion induced by image based on dimensional theory of emotion
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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A primary strength of the XCS approach is its ability to create maximally accurate general rules. In automatic target recognition (ATR) there is a need for robust performance beyond so-called standard operating conditions (SOCs, those conditions for which training data is available) to extended operating conditions (EOCs, conditions of known targets that cannot be foreseen and trained for). EOCs include things like vehicle-specific variations, environmental effects (mud, etc.), unanticipated viewing angles, and articulation of components of the target (hatches, turrets, etc.). This paper presents experiments where XCS addresses structural generalization over global and local features normally used in ATR classification. In many SOCs, these features are adequate for target recognition. Our goal with XCS is to form generalized rules that utilize these features for effective ATR in EOCs. Results show that XCS is effective in this generalization task. Conclusions and future directions for research are discussed.