About: Efficient Feature Subset Selection and Subset Size Optimization

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	Efficient Feature Subset Selection and Subset Size Optimization Efficient Feature Subset Selection and Subset Size Optimization (en)
skos:prefLabel	Efficient Feature Subset Selection and Subset Size Optimization Efficient Feature Subset Selection and Subset Size Optimization (en)
skos:notation	RIV/67985556:_____/10:00342820!RIV11-MSM-67985556
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(1M0572), P(2C06019), P(GA102/07/1594), P(GA102/08/0593), Z(AV0Z10750506)
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Pudil, Pavel Somol, Petr Novovičová, Jana
http://linked.open.../riv/druhVysledku	C - Kapitola v knize
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	256395
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/10:00342820
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	dimensionality reduction; pattern recognition; machine learning; feature selection; optimization; subset search; classification (en)
http://linked.open.../riv/klicoveSlovo	subset search classification dimensionality reduction optimization feature selection machine learning pattern recognition
http://linked.open...ontrolniKodProRIV	[C1A142E5C3AF]
http://linked.open...i/riv/mistoVydani	Vukovar, Croatia
http://linked.open...i/riv/nazevZdroje	Pattern Recognition, Recent Advances
http://linked.open...in/vavai/riv/obor	BD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...v/pocetStranKnihy	524 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Data, algorithms, decision making (Medical) Knowledge Acquisition and Modelling BTF Texture Modelling Recurrent Probabilistic Neural Networks
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Pudil, Pavel Somol, Petr Novovičová, Jana
http://linked.open...n/vavai/riv/zamer	Pokročilé matematické metody získávání, zpracování a využití informací a znalostí ve složitých a nedeterministických systémech
number of pages	23 (xsd:int)
http://purl.org/ne...btex#hasPublisher	In-Teh
https://schema.org/isbn	978-953-7619-90-9

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