Defining Similarity Measures:
Top-Down vs. Bottom-Up

Armin Stahl

University of Kaiserslautern, Computer Science Department
Artificial Intelligence - Knowledge-Based Systems Group
67653 Kaiserslautern, Germany
stahl@informatik.uni-ki.de



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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