Branch & Bound algorithm with partial prediction for use with recursive and non-recursive criterion forms

0410760 20240103182237.220060210235959.9 3-540-41767-2 Branch & Bound algorithm with partial prediction for use with recursive and non-recursive criterion forms Heidelberg Springer 2001 10 s. Lecture Notes in Computer Science. 2013 Advances in Pattern Recognition - ICAPR 2001. Proceedings425-434SinghS.MurshedN.KropatschW. branch and bound search tree optimal subset selection 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*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/historie/somol-branch & bound algorithm with partial prediction for use with recursive and non-recursive criterion forms.pdf 09K 09J VS96063 GA MŠk cav_un_auth*0025066 ME 187 GA MŠk cav_un_auth*0028306 KSK1075601 GA AV ČR cav_un_auth*0027435 AV0Z1075907 We introduce a novel algorithm for optimal feature selection. As opposed to our recent Fast Branch & Bound (FBB) algorithm the new algorithm is well suitable for use with recursive criterion forms. Even if the new algorithm does not operate as effectively as the FBB algorithm, it is able to find the optimum significantly faster than any other Branch & Bound. cav_un_auth*0212824 ICAPR /2./ Rio de Janeiro BR 11.03.2001-14.03.2001 BD RO http://hdl.handle.net/11104/0130848 UTIA-B 20010229 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