0341554 20240111140738.720100325235959.9 10 s. www cav_un_epca*0341553978-0-88986-830-4CalgaryACTA Press2010Rafea Feature selection sequential search hybrid methods classification performance subset search statistical pattern recognition cav_un_auth*0101197 Somol Petr Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101171 Novovičová Jana Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101182 Pudil Pavel Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. PDF http://library.utia.cas.cz/separaty/2010/RO/somol-improving sequential feature selection methods performance by means of hybridization.pdf 942kb 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk CZ cav_un_auth*0216518 GA102/08/0593 GA ČR cav_un_auth*0239567 GA102/07/1594 GA ČR cav_un_auth*0228611 CEZ:AV0Z10750506 In this paper we propose the general scheme of defining hybrid feature selection algorithms based on standard sequential search with the aim to improve feature selection performance, especially on high-dimensional or large-sample data. We show experimentally that “hybridization” has not only the potential to dramatically reduce FS search time, but in some cases also to actually improve classifier generalization, i.e., its classification performance on previously unknown data. cav_un_auth*0261288 Advances in Computer Science and Engineering Sharm El Sheikh 15.03.2010-17.03.2010 EG 2011 BD http://hdl.handle.net/11104/0184495 2010 PDF 942kb cav_un_epca*0341553 Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering 978-0-88986-830-4 689-1-689-10 Proc. 6th IASTED Int. Conf. on Advances in Computer Science and Engineering Calgary ACTA Press 2010 Rafea 340