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Branch & Bound algorithm with partial prediction for use with recursive and non-recursive criterion forms
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SYSNO ASEP 0410760 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Branch & Bound algorithm with partial prediction for use with recursive and non-recursive criterion forms Author(s) Somol, Petr (UTIA-B) RID
Pudil, Pavel (UTIA-B) RID
Grim, Jiří (UTIA-B) RID, ORCIDIssue data Heidelberg: Springer, 2001 ISBN 3-540-41767-2 Source Title Advances in Pattern Recognition - ICAPR 2001. Proceedings / Singh S. ; Murshed N. ; Kropatsch W. Pages s. 425-434 Series Lecture Notes in Computer Science. Series number 2013 Number of pages 10 s. Action ICAPR /2./ Event date 11.03.2001-14.03.2001 VEvent location Rio de Janeiro Country BR - Brazil Event type WRD Language eng - English Country DE - Germany Keywords branch and bound ; search tree ; optimal subset selection Subject RIV BD - Theory of Information R&D Projects VS96063 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) ME 187 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) KSK1075601 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ 1075907 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
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