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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Multi-Objective Neural Network Optimization for Visual Object Detection
verfasst von : Stefan Roth, Alexander Gepperth, Christian Igel
Erschienen in: Multi-Objective Machine Learning
Verlag: Springer Berlin Heidelberg
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In real-time computer vision, there is a need for classifiers that detect patterns fast and reliably. We apply multi-objective optimization (MOO) to the design of feed-forward neural networks for real-world object recognition tasks, where computational complexity and accuracy define partially conflicting objectives. Evolutionary structure optimization and pruning are compared for the adaptation of the network topology. In addition, the results of MOO are contrasted to those of a single-objective evolutionary algorithm. As a part of the evolutionary algorithm, the automatic adaptation of operator probabilities in MOO is described.