Description du livre Taschenbuch. 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 r�f. du vendeur 9783319344454

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Realtime Data Mining : Self-learning Techniques for Recommendation Engines

Paprotny, Alexander; Thess, Michael

Edit� par Birkh�user (2016)

ISBN 10 : 3319344455 ISBN 13 : 9783319344454

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Description du livre Etat : New. N� de r�f. du vendeur 27440293-n

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Realtime Data Mining

Alexander Paprotny|Michael Thess

Edit� par Springer International Publishing (2016)

ISBN 10 : 3319344455 ISBN 13 : 9783319344454

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Description du livre 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 r�f. du vendeur 448746064

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Realtime Data Mining : Self-Learning Techniques for Recommendation Engines

Michael Thess

Edit� par Springer International Publishing (2016)

ISBN 10 : 3319344455 ISBN 13 : 9783319344454

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Description du livre Taschenbuch. 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 r�f. du vendeur 9783319344454

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