000168684 001__ 168684
000168684 005__ 20260213191050.0
000168684 0248_ $$2sideral$$a75623
000168684 037__ $$aART-2004-75623
000168684 041__ $$aeng
000168684 100__ $$0(orcid)0000-0003-0630-4366$$aLozano, M.
000168684 245__ $$aUsing the Geometrical Distribution of Prototypes for Training Set Condensing
000168684 260__ $$c2004
000168684 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168684 5203_ $$aIn this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. Although the ultimate aim of the algorithms proposed here is to obtain a strongly reduced training set, the performance is empirically evaluated over nine real datasets by comparing the reduction rate and the classification accuracy with those of other condensing techniques.
000168684 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000168684 590__ $$a0.513$$b2004
000168684 591__ $$aCOMPUTER SCIENCE, THEORY & METHODS$$b53 / 70 = 0.757$$c2004$$dQ4$$eT3
000168684 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b58 / 77 = 0.753$$c2004$$dQ4$$eT3
000168684 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000168684 700__ $$aSánchez, JS.
000168684 700__ $$aPla, F.
000168684 773__ $$g3040 (2004), 618-627$$pLect. notes comput. sci.$$tLecture Notes in Computer Science$$x0302-9743
000168684 8564_ $$s326155$$uhttps://zaguan.unizar.es/record/168684/files/texto_completo.pdf$$yPostprint
000168684 8564_ $$s1621790$$uhttps://zaguan.unizar.es/record/168684/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000168684 909CO $$ooai:zaguan.unizar.es:168684$$particulos$$pdriver
000168684 951__ $$a2026-02-13-18:28:25
000168684 980__ $$aARTICLE