0410659 20240103182229.820060210235959.9 3-540-41767-2 Heidelberg Springer 2001 10 s. Lecture Notes in Computer Science. 2013 425-434SinghS.MurshedN.KropatschW. statistical pattern recognition feature selection methodology cav_un_auth*0021087 Kittler J. GB cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 09K 09H GA402/01/0981 GA ČR cav_un_auth*0008962 KSK1019101 GA AV ČR cav_un_auth*0000219 AV0Z1075907 Advances in feature selection methododology for statistical pattern recognition are presented and overviewed in the taxonomical framewrok. The methods discussed include latest variants of the Branch and Bound algorithm, enhanced Floating Search techniques and the simultaneous semiparametric pdf modelling and feature space selection method. cav_un_auth*0212824 ICAPR /2./ Rio de Janeiro BR 11.03.2001-14.03.2001 BB RO http://hdl.handle.net/11104/0130747 UTIA-B 20010128 2001 2001 Heidelberg Springer 3-540-41767-2 Advances in Pattern Recognition - ICAPR 2001. Proceedings 425 434 Singh S. 340 Murshed N. 340 Kropatsch W. 340