Defining adequate similarity measures is one of the most difficult tasks when developing CBR applications. Unfortunately, only a limited number of techniques for supporting this task by using machine learning techniques have been developed up to now. In this paper, a new framework for learning similarity measures is presented. The main advantage of this approach is its generality, because its application is not restricted to classification tasks in contrast to other already known algorithms. A first refinement of the introduced framework for learning feature weights is described and finally some preliminary experimental results are presented.
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Stahl, A. (2001). Learning Feature Weights from Case Order Feedback. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_35
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DOI: https://doi.org/10.1007/3-540-44593-5_35
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