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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Reducing Training Sets by NCN-based Exploratory Procedures
verfasst von : M. Lozano, José S. Sánchez, Filiberto Pla
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques.