168685 20260213191050.0 sideral 75695 ART-2004-75695 eng Lozano, M. (orcid)0000-0003-0630-4366 An Adaptive Condensing Algorithm based on Mixtures of Gaussians 2004 In this paper, a new adaptive approach to training set size reduction, estimating probability density functions is presented. This scheme consists of defining a very small number of prototypes that represent all the original instances, using mixtures of gaussians. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over eleven real datasets by comparing not only the reduction rate but also the classification accuracy Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Sotoca, JM. Sánchez, JS. Pla, F. 113 (2004), 225-232 Front. artif. intell. appl. Frontiers in Artificial Intelligence and Applications 0922-6389 125574 http://zaguan.unizar.es/record/168685/files/texto_completo.pdf Postprint 2128971 http://zaguan.unizar.es/record/168685/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:168685 articulos driver 2026-02-13-18:28:27 ARTICLE