Classification by discrete optimization

Abstract An approach to classification is described where an evaluation function is available. Deterministic classifications are evaluated by a heuristic function, and a special search procedure is applied to find a classification optimizing this function. A specific application to image segmentation is presented, including several examples. The major difference between this approach and previous optimization attempts is the use of deterministic ratter than probabilistic classifications.

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