Abstract
Abstract
This paper discusses synergistic classification systems (SCSs) for categorizing defects in wood sheets. These systems combine several classifiers to improve the performance achievable with individula classifiers. Different types of classifier could be employed in an SCS. Because of their superior generalization ability, neural classifiers were adopted in preference to rule-based classifiers. The paper describes the tuning of the parameters of the neural classifiers and the determination of the optimum number of classifier units and the best of five strategies for combining their outputs.
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