168684 20260213191050.0 sideral 75623 ART-2004-75623 eng Lozano, M. (orcid)0000-0003-0630-4366 Using the Geometrical Distribution of Prototypes for Training Set Condensing 2004 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. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 0.513 2004 COMPUTER SCIENCE, THEORY & METHODS 53 / 70 = 0.757 2004 Q4 T3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 58 / 77 = 0.753 2004 Q4 T3 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Sánchez, JS. Pla, F. 3040 (2004), 618-627 Lect. notes comput. sci. Lecture Notes in Computer Science 0302-9743 326155 http://zaguan.unizar.es/record/168684/files/texto_completo.pdf Postprint 1621790 http://zaguan.unizar.es/record/168684/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:168684 articulos driver 2026-02-13-18:28:25 ARTICLE