Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China) - Hardcover
Synopsis
Machine learning and data mining are inseparably connected with uncertainty. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty.
"synopsis" may belong to another edition of this title.
From the Back Cover
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."About this title" may belong to another edition of this title.
Search results for Uncertainty Modeling for Data Mining: A Label Semantics...
Stock Image
Uncertainty Modeling for Data Mining (Hardcover)
Published by
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
New
Hardcover
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: new. Hardcover. 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. Machine learning and data mining are inseparably connected with uncertainty. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783642412509
Stock Image
Uncertainty Modeling for Data Mining: A Label Semantics Approach
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: Brand New. 2014 edition. 291 pages. 9.29x6.30x0.94 inches. In Stock. Seller Inventory # __3642412505
Buy New
£ 88
£ 12.50 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Stock Image
Uncertainty Modeling for Data Mining (Hardcover)
Published by
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
New
Hardcover
Seller: AussieBookSeller, Truganina, VIC, Australia
Seller rating 5 out of 5 stars
Hardcover. Condition: new. Hardcover. 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. Machine learning and data mining are inseparably connected with uncertainty. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9783642412509
Buy New
£ 112.16
£ 27.75 shipping
Ships from Australia to U.S.A.
Ships from Australia to U.S.A.
Quantity: 1 available
Stock Image
Uncertainty Modeling for Data Mining: A Label Semantics Approach
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: Brand New. 2014 edition. 291 pages. 9.29x6.30x0.94 inches. In Stock. Seller Inventory # x-3642412505
Buy New
£ 129.14
£ 12.50 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Seller Image
Uncertainty Modeling for Data Mining
Published by
Springer Berlin Heidelberg, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
New
Hardcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. A new research direction of fuzzy set theory in data miningOne of the first monographs of studying the transparency of data mining modelsContains more than 60 figures and illustrations in order to explain complicated conceptsMach. Seller Inventory # 5059911
Stock Image
Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Seller rating 5 out of 5 stars
Condition: Fine. 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. Photos available upon request. Seller Inventory # ZB1314411
Seller Image
Uncertainty Modeling for Data Mining : A Label Semantics Approach
Published by
Springer, Berlin, Springer, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. Neuware - 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. Seller Inventory # 9783642412509