Items related to Realtime Data Mining: Self-Learning Techniques for...
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis) - Hardcover
Book 48 of 88: Applied and Numerical Harmonic Analysis
Synopsis
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.
"synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About this title" may belong to another edition of this title.
Search results for Realtime Data Mining: Self-Learning Techniques for...
Seller Image
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis) : Toward the Self-Learning Recommendation Engine
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Gebundene Ausgabe. Condition: Sehr gut. Gebraucht - Sehr gut sg - leichte beschädigungen oder verschmutzungen, ungelesenes mängelexemplar, gestempelt - 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 real-time data mining is currently developing at an exceptionally dynamic pace, and real-time 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, real-time 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 real-time 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 real-time 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. Seller Inventory # INF1000339798
Stock Image
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
Seller: Phatpocket Limited, Waltham Abbey, HERTS, United Kingdom
Seller rating 5 out of 5 stars
Condition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. Seller Inventory # Z1-AA-007-00637
Buy Used
£ 62.99
£ 10.64 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available
Stock Image
Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 19912161
Seller Image
Realtime Data Mining
Published by
Springer International Publishing Dez 2013, 2013
ISBN 10: 3319013203
ISBN 13: 9783319013206
New
Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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. Seller Inventory # 9783319013206
Stock Image
Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 19912161-n
Stock Image
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English. Seller Inventory # ria9783319013206_new
Buy New
£ 106.71
£ 11.29 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Seller Image
Realtime Data Mining
Published by
Springer International Publishing, 2013
ISBN 10: 3319013203
ISBN 13: 9783319013206
New
Hardcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Gebunden. Condition: 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. Seller Inventory # 4496116
Buy New
£ 80.07
£ 41.28 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
Condition: New. Realtime Data Mining Series: Applied and Numerical Harmonic Analysis. Num Pages: 313 pages, 12 black & white illustrations, 88 colour illustrations, 28 black & white tables, biograp. BIC Classification: PBWH; UFM; UNF. Category: (P) Professional & Vocational. Dimension: 243 x 158 x 24. Weight in Grams: 682. . 2013. 2013th Edition. hardcover. . . . . Seller Inventory # V9783319013206
Seller Image
Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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. Seller Inventory # 9783319013206
Seller Image
Realtime Data Mining
Published by
Birkhäuser, Springer Dez 2013, 2013
ISBN 10: 3319013203
ISBN 13: 9783319013206
New
Hardcover
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -¿¿¿¿Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Enginesfeatures 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.Thismonograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinkingby 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.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 340 pp. Englisch. Seller Inventory # 9783319013206