0342820 20240103193500.120100513235959.9 23 s. 524 cav_un_epca*0342819978-953-7619-90-975-98Vukovar, CroatiaIn-Teh2010HeroutA. dimensionality reduction pattern recognition machine learning feature selection optimization subset search classification 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. http://library.utia.cas.cz/separaty/2010/RO/somol-efficient feature subset selection and subset size optimization.pdf 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 A broad class of decision-making problems can be solved by learning approach. This can be a feasible alternative when neither an analytical solution exists nor the mathematical model can be constructed. In these cases the required knowledge can be gained from the past data which form the so-called learning or training set. Then the formal apparatus of statistical pattern recognition can be used to learn the decision-making. The first and essential step of statistical pattern recognition is to solve the problem of feature selection (FS) or more generally dimensionality reduction (DR). The chapter summarizes the state of art in feature selection, addressing key topics including: FS categorization, FS criteria, FS search strategies, FS stability. 2011 BD http://hdl.handle.net/11104/0185446 2010 cav_un_epca*0342819 Pattern Recognition, Recent Advances 978-953-7619-90-9 75 98 Vukovar, Croatia In-Teh 2010 Herout A. 340