Using the Geometrical Distribution of Prototypes for Training Set Condensing
In this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithms proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing the reduction rate and the classification accuracy with those of other condensing techniques.
KeywordsVoronoi Diagram Near Neighbour Decision Boundary Geometrical Distribution Pattern Recognition Letter
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