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ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib T. M. Caelli and A. Pennington An improved rule generation method for evidence-based classification systems ABSTRACT A new method is described for generating rules which attempt to optimize classification when class samples are not contiguous nor necessarily segregated in feature space. The method combines well-known clustering techniques (Leader and K-Means methods) with Stochastic Relaxation to minimize a combined cluster entropy function. Further, a technique is developed which is capable of determining the cluster weights which optimize classification performance and reflect the Boolean structures of the associated convex clusters.

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