“Change-glasses” approach in pattern recognition
Pattern Recognition Letters
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Development of Situation-Aware Application Software for Ubiquitous Computing Environment
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Situation-Aware Contract Specification Language for Middleware for Ubiquitous Computing
FTDCS '03 Proceedings of the The Ninth IEEE Workshop on Future Trends of Distributed Computing Systems
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Fuzzy ARTMAP network with evolutionary learning
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
A context model for ubiquitous computing applications
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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This paper proposes a novel framework for adaptive and intelligent systems that can be used under dynamic and uneven environments by taking advantage of environment context identification. Adaptation to dynamically changing environments is very important since advanced applications become pervasive and ubiquitous. The proposed framework, callesd CAES (Context-Aware Evolvable System), adopts the concept of context-aware and the evolutionary computing, and the system working environments are learned (clustered) and identified as environmental contexts. The context-awareness has been carried out by unsupervised learning, Fuzzy ART. Genetic algorithm (GA) is used to explore the most effective action configuration for each identified context. The knowledge of the individual context and its associated chromosomes representing optimal action configurations is accumulated and stored in the context knowledge base. Once the context knowledge is constructed, the system can adapt to varying environment in real-time. The framework of CAES has been tested in the area of intelligent vision application where most approaches show vulnerability under dynamically changing environments. The superiority of the proposed scheme is shown using three face image data sets: Inha, FERET, and Yale.