UNCERTAINTY MODELING FOR DATA MINING: A LABEL SEMANTICS APPROACH Unknown - 2014
de QIN ZENGCHANG ET.AL
Descripción de contraportada
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.
Detalles
- Título UNCERTAINTY MODELING FOR DATA MINING: A LABEL SEMANTICS APPROACH
- Autor QIN ZENGCHANG ET.AL
- Encuadernación unknown
- Editorial Springer
- Fecha de publicación 2014
- Features Illustrated
- ISBN 9783642412509
-
Temas
- Aspects (Academic): Science/Technology Aspects
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