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dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorAiken, Jim
dc.identifier.citationJournal of Experimental & Theoretical Artificial Intelligence. Volumen 16 (3), pp. 145-160. Informa UK Limited.
dc.identifier.issn0952-813X (Print) / 1362-3079 (Online)
dc.description.abstractCase-based reasoning systems, in general, and instance-based reasoning systems, in particular, are used more and more in industrial applications nowadays. During the last few years, researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a method for automating the retrieval stage and indexation of instance-based reasoning systems. This method is based on a modification of a new type of topology preserving map that can be used for scale invariant classifica- tion. The scale invariant map is an implementation of the negative feedback network to form a topology-preserving mapping. Maximum/minimum likelihood learning is applied in this paper to the scale invariant map and its possibilities are explored. This method automates the organization of cases and the retrieval stage of case-based reasoning systems. The proposed methodology groups instances with similar struc- ture, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.
dc.publisherInforma UK Limited
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titleIBR retrieval method based on topology preserving mappings

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported