By Josep Lluís Arcos, Dolores Cañamero, Ramon López de Mántaras (auth.), Klaus-Dieter Althoff, Ralph Bergmann, L.Karl Branting (eds.)
The biennial overseas convention on Case-Based Reasoning (ICCBR) - ries, which started in Sesimbra, Portugal, in 1995, was once meant to supply a global discussion board for the simplest basic and utilized study in case-based reasoning (CBR). It used to be was hoping that this type of discussion board might motivate the g- wth and rigor of the eld and triumph over the former tendency towards remoted nationwide CBR groups. The foresight of the unique ICCBR organizers has been rewarded by way of the expansion of a full of life and cosmopolitan CBR neighborhood. CBR is now widely known as a robust and critical computational approach for a variety of sensible functions. through selling an trade of rules between CBR researchers from around the globe, the ICCBR sequence has facilitated the wider reputation and use of CBR. ICCBR-99 has persevered this custom by way of attracting top quality study and functions papers from around the globe. Researchers from 21 nations submitted eighty papers to ICCBR-99. From those submissions, 17 papers have been chosen for lengthy oral presentation, 7 have been approved for brief oral presentation, and 19 papers have been authorised as posters. This quantity units forth those forty three papers, which include either mature paintings and leading edge new ideas.
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Extra info for Case-Based Reasoning Research and Development: Third International Conference on Case-Based Reasoning, ICCBR-99 Seeon Monastery, Germany, July 27-30, 1999 Proceedings
44-58, 1999 Springer-Verlag Berlin Heidelberg 1999 A Multiple-Domain Evaluation of Stratified Case-Based Reasoning 45 This paper describes two sets of experiments in which SCBR was applied to alternative domains. Section 2 describes the application of SCBR to a problem in combinatorial optimization, sorting by prefix reversal. Section 3 describes a prototype application of SCBR to logistics planning. These experiments suggest that optimal-case utility is a key factor in the applicability of SCBR.
For example, the set of ranked pairs fPu घA; B ङ; Pu घC; Dङ; Pu घE; F ङg can be represented as shown in Figure 1 ऐ ऐ ऐ ऐ by the preference arcs AB , CD, and EF घwhere AB इ Pu घA; B ङङ. feature1 D C A X E B Y F feature2 ऐ X is ranked higherऐ than Y by 1ARC because of the ऐ matchऐbetween hypothesis XY and preference arc EF . The dissimilarity between XY and EF is the sum of the Euclidean distances represented by dotted lines. Fig. 1. In the 1ARC ऐ algorithm, ऐ a new pair of objects, X and Y is ranked by determining whether XY or Y X has the better match toऐ a ranked pair in the training set.
In the present work we carried on experiments with two diæerent estimators. The ærst is the Naive Bayes Estimator that is the estimator that is implicit in the Naive Bayes Classiæer. It is a natural estimator for nominal feature and it can be extended to the numeric ones by discretization. The second is the Gaussian Kernel Estimator, a noníparametric density estimator that in its original formulation uses the Euclidean metric. In order to extend the density estimation technique to nominal features the Euclidean metric is simply substituted by HEOM and the densities are supposed to replace the probabilities in the expressions of the metrics.