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Pattern Recognition, Recent Advances
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SYSNO ASEP 0342820 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Efficient Feature Subset Selection and Subset Size Optimization Author(s) Somol, Petr (UTIA-B) RID
Novovičová, Jana (UTIA-B)
Pudil, Pavel (UTIA-B) RIDSource Title Pattern Recognition, Recent Advances. - Vukovar, Croatia : In-Teh, 2010 / Herout A. - ISBN 978-953-7619-90-9 Pages s. 75-98 Number of pages 23 s. Number of copy 201 Number of pages 524 Language eng - English Country HR - Croatia Keywords dimensionality reduction ; pattern recognition ; machine learning ; feature selection ; optimization ; subset search ; classification Subject RIV BD - Theory of Information R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) GA102/07/1594 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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