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Rough Sets and Knowledge Technology : 8th International Conference, RSKT 2013, Halifax, NS, Canada, October 11-14, 2013, Proceedings / edited by Pawan Lingras, Marcin Wolski, Chris Cornelis, Sushmita Mitra, Piotr Wasilewski.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Contributor:
Lingras, Pawan, Editor.
Wolski, Marcin, Editor.
Cornelis, Chris, Editor.
Mitra, Sushmita, Editor.
Wasilewski, Piotr, Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 8171
Lecture Notes in Artificial Intelligence, 2945-9141 ; 8171
Language:
English
Subjects (All):
Artificial intelligence.
Numerical analysis.
Data mining.
Image processing-Digital techniques.
Computer vision.
Artificial Intelligence.
Numerical Analysis.
Data Mining and Knowledge Discovery.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Local Subjects:
Artificial Intelligence.
Numerical Analysis.
Data Mining and Knowledge Discovery.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Physical Description:
1 online resource (XIV, 414 pages) : 84 illustrations
Edition:
1st ed. 2013.
Contained In:
Springer Nature eBook
Place of Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
System Details:
text file PDF
Summary:
This book constitutes the thoroughly refereed conference proceedings of the 8th International Conference on Rough Sets and Knowledge Technology, RSKT 2013, held in Halifax, Canada in October 2013 as one of the co-located conferences of the 2013 Joint Rough Set Symposium, JRS 2013. The 69 papers (including 44 regular and 25 short papers) included in the JRS proceedings (LNCS 8170 and LNCS 8171) were carefully reviewed and selected from 106 submissions. The papers in this volume cover topics such as history and future of rough sets; foundations and probabilistic rough sets; rules, reducts, ensembles; new trends in computing; three-way decision rough sets; and learning, predicting, modeling.
Contents:
History and future of rough sets
Foundations and probabilistic rough sets
Rules, reducts, ensembles
New trends in computing
Three-way decision rough sets
Learning, predicting, modeling.
Other Format:
Printed edition:
ISBN:
978-3-642-41299-8
9783642412998
Access Restriction:
Restricted for use by site license.

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