Improving statistical measures of feature subsets by conventional and evolutionary approaches

0410444 20240103182214.520060210235959.9 3-540-67946-4 Improving statistical measures of feature subsets by conventional and evolutionary approaches Berlin Springer 2000 10 s. Lecture Notes in Computer Science. 1876 Advances in Pattern Recognition. Proceedings77-86FerriJ. F.InestaM. J.AminA.PudilP. cav_un_auth*0212702 Mayer H. AT cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0212703 Huber R. AT cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 12B 09K AKTION 23p20 MŠMT CZ KONTAKT ME 187/98 MŠMT CZ VS96063 MŠMT CZ cav_un_auth*0025066 AV0Z1075907 Comparative analysis of recently developed and highly effective sequential feature selection algorithms with approaches based on evolutionary algorithms, enabling parallel feature subset selection, is presented. cav_un_auth*0212672 Joint IAPR International Workshops SSPR 2000 and SPR 2000 Alicante ES 30.08.2000-01.09.2000 JC RO http://hdl.handle.net/11104/0130533 UTIA-B 20000160 2000 2000 Berlin Springer 3-540-67946-4 Advances in Pattern Recognition. Proceedings 77 86 Ferri J. F. 340 Inesta M. J. 340 Amin A. 340 Pudil P. 340