Feature Selection for Data and Pattern Recognition

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of act...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Stańczyk, Urszula (Επιμελητής έκδοσης), Jain, Lakhmi C. (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015.
Σειρά:Studies in Computational Intelligence, 584
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Feature Selection for Data and Pattern Recognition  |h [electronic resource] /  |c edited by Urszula Stańczyk, Lakhmi C. Jain. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2015. 
300 |a XVIII, 355 p. 74 illus., 20 illus. in color.  |b online resource. 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 584 
505 0 |a Feature Selection for Data and Pattern Recogniton: an Introduction -- Part I Estimation of Feature Importance -- Part II Rough Set Approach to Attribute Reduction -- Part III Rule Discovery and Evaluation -- Part IV Data- and Domain-oriented Methodologies. 
520 |a This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
700 1 |a Stańczyk, Urszula.  |e editor. 
700 1 |a Jain, Lakhmi C.  |e editor. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783662456194 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 584 
856 4 0 |u http://dx.doi.org/10.1007/978-3-662-45620-0  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)