Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)

Qin, Zengchang, Tang, Yongchuan

ISBN 10: 3642412505 / ISBN 13: 9783642412509

Verlag: Springer, 2014

Beschreibung

310 pp., hardcover, previous owner's name neatly inked to the title page, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Bestandsnummer des Verkäufers ZB1314411

Bibliografische Details

• Titel: Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
• Verlag: Springer
• Veröffentlichungsdatum: 2014
• Sprache: Englisch
• ISBN-10: 3642412505
• ISBN-13: 9783642412509
• Bindung: Hardcover
• Zustand: Fine

Über diesen Titel

Inhaltsangabe

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.