Advances in Case-Based Reasoning: 6th European Conference, by Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun

By Sarah Zelikovitz, Haym Hirsh (auth.), Susan Craw, Alun Preece (eds.)

The papers accrued during this quantity have been provided on the sixth eu C- ference on Case-Based Reasoning (ECCBR 2002) held on the Robert Gordon college in Aberdeen, united kingdom. This convention a sequence of very succe- ful well-established biennial eu workshops held in Trento, Italy (2000), Dublin, eire (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the preliminary workshop in Kaiserslautern, Germany (1993). those conferences have a historical past of attracting ?rst-class eu and foreign researchers and practitioners within the years interleaving with the biennial overseas co- terpart ICCBR; the 4th ICCBR convention used to be held in Vancouver, Canada in 2001. court cases of ECCBR and ICCBR meetings are frequently released by way of Springer-Verlag of their LNAI sequence. Case-Based Reasoning (CBR) is an AI problem-solving procedure the place pr- lems are solved via retrieving and reusing suggestions from related, formerly solved difficulties, and probably revising the retrieved option to re?ect di?erences - tween the hot and retrieved difficulties. Case wisdom shops the formerly solved difficulties and is the most wisdom resource of a CBR process. a first-rate concentration of CBR learn is the illustration, acquisition and upkeep of case wisdom. lately different wisdom assets were famous as vital: indexing, similarity and model wisdom. Signi?cant wisdom engine- ing e?ort can be wanted for those, and so the illustration, acquisition and upkeep of CBR wisdom extra ordinarily became important.

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Additional resources for Advances in Case-Based Reasoning: 6th European Conference, ECCBR 2002 Aberdeen, Scotland, UK, September 4–7, 2002 Proceedings

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G. a desired maximum price), as well as ‘ideal’ values. Diversity of retrieval may be an additional criterion, again with particular application to product recommender systems [8]. In product recommendation, similarity between the retrieved cases and the customer’s ‘ideal’ values is desirable, but too much similarity between the retrieved cases themselves may be less desirable. If the customer is not satisfied with the most similar case in the result set, for example, the chances of him or her being satisfied with an alternative case in the result set are increased if a diverse set is recommended.

The diagnostic profiles learned in Algorithm Lcp will also contain rare findings. In a second step we will remove these unfrequent findings from the profile. Before that, we will consider similarities between findings in the profile. For example, if a coarse profile includes the finding pain=high (p=h) with frequency Inductive Learning for Case-Based Diagnosis with Multiple Faults 35 Algorithm 1. g. 5). But, since both findings are very similar to each other, an adapted frequency may be sufficiently frequent to remain in the profile.

Section 3 introduces some example data that will be used in subsequent sections of the paper. Section 4 illustrates the operation of OBR and argues that it is more expressive than pure similarity-based retrieval. Section 5 report the results of a comparison between OBR and the approach described in [9] & [1].

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