Adaptive eye location using FuzzyART
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Adaptive context-aware filter fusion for face recognition on bad illumination
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Adaptive object recognition using context-aware genetic algorithm under dynamic environment
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
On the filter combination for efficient image preprocessing under uneven illumination
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Filter selection and identification similarity using clustering under varying illumination
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
An efficient classifier fusion for face recognition including varying illumination
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Context-Aware evolvable system framework for environment identifying systems
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Face recognition using correlation between illuminant context
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Neural networks, particularly the multilayer perceptron, have been used extensively in automated signal classification systems with classification accuracy as the figure of merit. Three important issues that can enhance the utility of these systems are (i) incremental learning, (ii) confidence or reliability measures and (iii) performance improvement through continual learning. This paper investigates these issues using a fuzzy ARTMAP network. A hypothesis testing based algorithm is developed for computing reliability measures, which are fed back to the network for retraining and performance improvement. Implementation results on ultrasonic data are presented.