In machine learning and numerical optimization, there has been an ongoing debate about properties of local optima and the impact of these properties on generalization. In this paper, we make a first attempt to address this question for case-based reasoning systems, more specifically for instance-based learning as it takes place in the retain phase. In so doing, we cast case learning as an optimization problem, develop a notion of local optima, propose a measure for the flatness or sharpness of these optima and empirically evaluate the relation between sharp minima and the generalization performance of the corresponding learned case base.
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