By Edwina L. Rissland (auth.), Thomas R. Roth-Berghofer, Mehmet H. Göker, H. Altay Güvenir (eds.)
This ebook constitutes the refereed court cases of the eighth eu convention on Case-Based Reasoning, ECCBR 2004, held in Fethiye, Turkey in September 2006.
The 31 revised complete papers and five revised program papers awarded including 2 invited papers and a couple of abstracts of invited talks have been conscientiously reviewed and chosen from a variety of submissions. All present matters in case-based reasoning, starting from theoretical and methodological concerns to complicated functions in numerous fields are addressed, therefore offering a consultant image of present CBR research.
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Extra info for Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings
Screen size). , screen = 15, type = laptop). An ideal value for a NIB or nominal attribute, if any, is treated as an equality constraint and also provides the basis for assessment of a given case’s similarity with respect to the attribute. Assessment of similarity with respect to LIB/MIB attributes for which upper/lower limits are provided in a given query is based on assumed preferences [3, 28]. That is, the preferred value of a LIB attribute is assumed to be the lowest value in the case base, while the preferred value of a MIB attribute is assumed to be the highest value in the case base.
3. Constructing the retrieval set in compromise driven retrieval First, the most similar case is placed in the retrieval set and any cases that satisfy a subset of the constraints satisfied by this case are eliminated from the list of candidate cases. The most similar of the remaining cases is now added to the retrieval set and any cases that satisfy a subset of the constraints satisfied by that case are eliminated. This process continues until no further cases remain. A detail not shown in Fig.
In reality most users do not care whether a solution deploys CBR or not as long as it solves the problem and has a clear business beneﬁt. Actually, most do not know that it is CBR. This coincided with the insight that the CBR engine forms only a fraction of the solution. g. PATDEX and even CBRWorks, were appropriate to understand the principles, but insuﬃcient for real world deployment. e. orenge or e:IAS, as it is called today. Since then, we had to add a multitude of ingredients to make it a success, for example: – – – – – – – – – – – textual and structured CBR, rules and decision trees (the obvious) lingustics for information extraction and full text search associative search and classiﬁcation text summarisation and keyword extraction language detection distributed processing and load balancing for scalability interfaces for all sorts of data sources interfaces for building applications APIs for all sorts of front ends a component architecture to plug it all together services to ﬁt it into the SOA world The above list is in no way complete.
Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings by Edwina L. Rissland (auth.), Thomas R. Roth-Berghofer, Mehmet H. Göker, H. Altay Güvenir (eds.)