Articoli correlati a Uncertainty Modeling for Data Mining: A Label Semantics...
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Dalla quarta di copertina
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Uncertainty Modeling for Data Mining: A Label Semantics...
Foto dell'editore
Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
Da: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Valutazione del venditore 5 su 5 stelle
Condizione: 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. Codice articolo ZB1314411
Compra usato
EUR 94,27
Spedizione EUR 3,88
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Uncertainty Modeling for Data Mining (Hardcover)
Editore:
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
Nuovo
Rilegato
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: 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. Codice articolo 9783642412509
Compra nuovo
EUR 106,56
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 1 disponibili
Foto dell'editore
Uncertainty Modeling for Data Mining: A Label Semantics Approach (Advanced Topics in Science and Technology in China)
Da: California Books, Miami, FL, U.S.A.
Valutazione del venditore 4 su 5 stelle
Condizione: New. Codice articolo I-9783642412509
Compra nuovo
EUR 116,39
Spedizione gratuita
Spedito in U.S.A.
Spedito in U.S.A.
Quantità: 7 disponibili
Foto dell'editore
Uncertainty Modeling for Data Mining: A Label Semantics Approach
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: Brand New. 2014 edition. 291 pages. 9.29x6.30x0.94 inches. In Stock. Codice articolo __3642412505
Compra nuovo
EUR 105,75
Spedizione EUR 14,58
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 1 disponibili
Immagini fornite dal venditore
Uncertainty Modeling for Data Mining
Da: moluna, Greven, Germania
Valutazione del venditore 5 su 5 stelle
Condizione: 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. Codice articolo 5059911
Compra nuovo
EUR 116,38
Spedizione EUR 48,99
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 5 disponibili
Foto dell'editore
Uncertainty Modeling for Data Mining: A Label Semantics Approach
Da: Revaluation Books, Exeter, Regno Unito
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: Brand New. 2014 edition. 291 pages. 9.29x6.30x0.94 inches. In Stock. Codice articolo x-3642412505
Compra nuovo
EUR 153,52
Spedizione EUR 14,58
Spedito da Regno Unito a U.S.A.
Spedito da Regno Unito a U.S.A.
Quantità: 2 disponibili
Foto dell'editore
Uncertainty Modeling for Data Mining (Hardcover)
Editore:
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2014
ISBN 10: 3642412505
ISBN 13: 9783642412509
Nuovo
Rilegato
Da: AussieBookSeller, Truganina, VIC, Australia
Valutazione del venditore 5 su 5 stelle
Hardcover. Condizione: 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. Codice articolo 9783642412509
Compra nuovo
EUR 146,18
Spedizione EUR 31,92
Spedito da Australia a U.S.A.
Spedito da Australia a U.S.A.
Quantità: 1 disponibili