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- ÉditeurBirkhauser Verlag AG
- Date d'édition2014
- ISBN 10 3319013203
- ISBN 13 9783319013206
- ReliureRelié
- Numéro d'édition1
- Nombre de pages313
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
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Birkhäuser
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Description du livre Etat : New. Book is in NEW condition. N° de réf. du vendeur 3319013203-2-1
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
Edité par
Birkhäuser
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
Neuf
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Description du livre Etat : New. New! This book is in the same immaculate condition as when it was published. N° de réf. du vendeur 353-3319013203-new
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EUR 102,34
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Realtime Data Mining: Self-Learning Techniques For Recommendation Engines
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Birkhäuser
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Description du livre Etat : New. Brand New Original US Edition.We Ship to PO BOX Address also. EXPEDITED shipping option also available for faster delivery.This item may ship from the US or other locations in India depending on your location and availability. N° de réf. du vendeur ABTR-85664
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EUR 102,37
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Realtime Data Mining: Self-Learning Techniques For Recommendation Engines
Edité par
Birkhäuser
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Description du livre Etat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed. This item may ship from the US or our Overseas warehouse depending on your location and stock availability. We Ship to PO BOX Location also. N° de réf. du vendeur ABRR-85664
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis) by Paprotny, Alexander, Thess, Michael [Hardcover ]
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(2014)
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ISBN 13 : 9783319013206
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Description du livre Hardcover. Etat : new. N° de réf. du vendeur 9783319013206
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Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
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(2013)
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ISBN 13 : 9783319013206
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Description du livre Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. N° de réf. du vendeur ria9783319013206_lsuk
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Realtime Data Mining
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Springer International Publishing Dez 2013
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Description du livre Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware - Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's 'classic' data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization. 340 pp. Englisch. N° de réf. du vendeur 9783319013206
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EUR 106,99
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Realtime Data Mining
Edité par
Springer International Publishing
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
Neuf
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Description du livre Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Specifically addresses recommendation engines from a mathematically rigorous viewpointDiscusses a control-theoretic framework for recommendation enginesProvides applications to a number of areas within engineering and computer science. N° de réf. du vendeur 4496116
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EUR 92,27
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Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
Edité par
Springer International Publishing
(2013)
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
Neuf
Couverture rigide
Quantité disponible : 1
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Evaluation vendeur
Description du livre Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's 'classic' data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization. N° de réf. du vendeur 9783319013206
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EUR 111,07
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