Uncertainty Modeling for Data Mining (inbunden)

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Format: Inbunden (Hardback)
Språk: Engelska
Antal sidor: 291
Utgivningsdatum: 2014-03-07
Upplaga: 2014 ed.
Förlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Illustratör/Fotograf: Bibliographie 70 schwarz-weiße Abbildungen
Illustrationer: XIX, 291 p.
Dimensioner: 234 x 157 x 23 mm
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Antal komponenter: 1
Komponenter: 1 Hardback
ISBN: 9783642412509

Uncertainty Modeling for Data Mining

Name: Uncertainty Modeling for Data Mining
Price: 1135.00 SEK
Availability: InStock
Author: Zengchang Qin, Yongchuan Tang
ISBN: 9783642412509

A Label Semantics Approach

av Zengchang Qin, Yongchuan Tang

Inbunden, Engelska, 2014-03-07

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Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning. Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.

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This book constitutes the refereed proceedings of the International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, IUKM 2013, held in Beijing China, in July 2013. The 19 revised full papers were carefully reviewed a...

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