大葉大學圖書館 |

Feature selection for data and pattern recognition[electronic resource] /

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	006.4
[NT 47271] Title/Author:	Feature selection for data and pattern recognition/ edited by Urszula Stanczyk, Lakhmi C. Jain.
[NT 51406] other author:	Stanczyk, Urszula.
出版者:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2015.
面页册数:	xviii, 355 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
标题:	Pattern recognition systems.
标题:	Rough sets.
标题:	Engineering.
标题:	Computational Intelligence.
标题:	Artificial Intelligence (incl. Robotics)
ISBN:	9783662456200 (electronic bk.)
ISBN:	9783662456194 (paper)
[NT 15000228] null:	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.
[NT 15000229] null:	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.
电子资源:	http://dx.doi.org/10.1007/978-3-662-45620-0

Feature selection for data and pattern recognition[electronic resource] /
Feature selection for data and pattern recognition[electronic resource] /edited by Urszula Stanczyk, Lakhmi C. Jain. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xviii, 355 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.5841860-949X ;. - Studies in computational intelligence ;v.379..

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.

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.

ISBN: 9783662456200 (electronic bk.)

Standard No.: 10.1007/978-3-662-45620-0doiSubjects--Topical Terms:

189561
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Feature selection for data and pattern recognition[electronic resource] /
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