Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach
Language: English
Published by Springer, 2016
- Softcover
- Used
Seller: Buchpark, Trebbin, GermanyBuchpark
AbeBooks seller since September 30, 2021
Condition: Used - Fine
US$ 45.50
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Add to basketItem description from seller
Zustand: Sehr gut | Seiten: 364 | Sprache: Englisch | Produktart: Bücher | A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on ¿Inductive Constraint Programming¿ and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.…
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- Title
- Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach
- Author
- Unbekannt
- Publisher
- Springer
- Publication year
- 2016
- Condition
- Sehr gut
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3319501364
- ISBN 13
- 9783319501369
- Seller catalogs
- Bücher
A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.
This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.
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