Abstract
Defining similarity measures is a crucial task when developing CBR applications. Particularly, when employing utility-based similarity measures rather than pure distance-based measures one is confronted with a difficult knowledge engineering task. In this paper we point out some problems of the state-of-the-art procedure to defining similarity measures. To overcome these problems we propose an alternative strategy to acquire the necessary domain knowledge based on a Machine Learning approach. To show the feasibility of this strategy several application scenarios are discussed and some results of an experimental evaluation for one of these scenarios are presented.
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Stahl, A. (2002). Defining Similarity Measures: Top-Down vs. Bottom-Up. In: Craw, S., Preece, A. (eds) Advances in Case-Based Reasoning. ECCBR 2002. Lecture Notes in Computer Science(), vol 2416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46119-1_30
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DOI: https://doi.org/10.1007/3-540-46119-1_30
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