000168685 001__ 168685 000168685 005__ 20260213191050.0 000168685 0248_ $$2sideral$$a75695 000168685 037__ $$aART-2004-75695 000168685 041__ $$aeng 000168685 100__ $$0(orcid)0000-0003-0630-4366$$aLozano, M. 000168685 245__ $$aAn Adaptive Condensing Algorithm based on Mixtures of Gaussians 000168685 260__ $$c2004 000168685 5060_ $$aAccess copy available to the general public$$fUnrestricted 000168685 5203_ $$aIn this paper, a new adaptive approach to training set size reduction, estimating probability density functions is presented. This scheme consists of defining a very small number of prototypes that represent all the original instances, using mixtures of gaussians. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over eleven real datasets by comparing not only the reduction rate but also the classification accuracy 000168685 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000168685 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000168685 700__ $$aSotoca, JM. 000168685 700__ $$aSánchez, JS. 000168685 700__ $$aPla, F. 000168685 773__ $$g113 (2004), 225-232$$pFront. artif. intell. appl.$$tFrontiers in Artificial Intelligence and Applications$$x0922-6389 000168685 8564_ $$s125574$$uhttps://zaguan.unizar.es/record/168685/files/texto_completo.pdf$$yPostprint 000168685 8564_ $$s2128971$$uhttps://zaguan.unizar.es/record/168685/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000168685 909CO $$ooai:zaguan.unizar.es:168685$$particulos$$pdriver 000168685 951__ $$a2026-02-13-18:28:27 000168685 980__ $$aARTICLE