Abstract
Although similarity measures play a crucial role in CBR applications, clear methodologies for defining them have not been developed yet. One approach to simplify the definition of similarity measures involves the use of machine learning techniques. In this paper we investigate important aspects of these approaches in order to support a more goal-directed choice and application of existing approaches and to initiate the development of new techniques. This investigation is based on a novel formal generalization of the classic CBR cycle, which allows a more suitable analysis of the requirements, goals, assumptions and restrictions that are relevant for learning similarity measures.
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Stahl, A. (2005). Learning Similarity Measures: A Formal View Based on a Generalized CBR Model. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_39
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DOI: https://doi.org/10.1007/11536406_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28174-0
Online ISBN: 978-3-540-31855-2
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