000168687 001__ 168687 000168687 005__ 20260213191050.0 000168687 0248_ $$2sideral$$a75622 000168687 037__ $$aART-2003-75622 000168687 041__ $$aeng 000168687 100__ $$0(orcid)0000-0003-0630-4366$$aLozano, M. 000168687 245__ $$aReducing Training Sets by NCN-based Exploratory Procedures 000168687 260__ $$c2003 000168687 5060_ $$aAccess copy available to the general public$$fUnrestricted 000168687 5203_ $$aIn this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithm proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques. 000168687 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000168687 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000168687 700__ $$aSánchez, JS. 000168687 700__ $$aPla, F. 000168687 773__ $$g2652 (2003), 453-461$$pLect. notes comput. sci.$$tLecture Notes in Computer Science$$x0302-9743 000168687 8564_ $$s281104$$uhttps://zaguan.unizar.es/record/168687/files/texto_completo.pdf$$yPostprint 000168687 8564_ $$s1540705$$uhttps://zaguan.unizar.es/record/168687/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000168687 909CO $$ooai:zaguan.unizar.es:168687$$particulos$$pdriver 000168687 951__ $$a2026-02-13-18:28:29 000168687 980__ $$aARTICLE