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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines - Couverture rigide
Livre 48 sur 88: Applied and Numerical Harmonic Analysis
Synopsis
1 Brave New Realtime World - Introduction.- 2 Strange Recommendations? - On The Weaknesses Of Current Recommendation Engines.- 3 Changing Not Just Analyzing - Control Theory And Reinforcement Learning.- 4 Recommendations As A Game - Reinforcement Learning For Recommendation Engines.- 5 How Engines Learn To Generate Recommendations - Adaptive Learning Algorithms.- 6 Up The Down Staircase - Hierarchical Reinforcement Learning.- 7 Breaking Dimensions - Adaptive Scoring With Sparse Grids.- 8 Decomposition In Transition - Adaptive Matrix Factorization.- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization.- 10 The Big Picture - Towards A Synthesis Of Rl And Adaptive Tensor Factorization.- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests.- 12 Building A Recommendation Engine - The Xelopes Library.- 13 Last Words - Conclusion.- References.- Summary Of Notation.
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis) : Toward the Self-Learning Recommendation Engine
Edité par
Springer-Verlag Gmbh, 2013
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Gebundene Ausgabe. Etat : 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. N° de réf. du vendeur INF1000339798
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
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Birkh�user, 2013
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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Etat : 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. N° de réf. du vendeur Z1-AA-007-00637
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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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Realtime Data Mining
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Springer International Publishing Dez 2013, 2013
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
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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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Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
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Etat : 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. . . . . N° de réf. du vendeur V9783319013206
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Realtime Data Mining
Edité par
Springer International Publishing, 2013
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
Neuf
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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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Realtime Data Mining: Self-Learning Techniques for Recommendation Engines (Applied and Numerical Harmonic Analysis)
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Etat : New. In English. N° de réf. du vendeur ria9783319013206_new
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Realtime Data Mining : Self-Learning Techniques for Recommendation Engines
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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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Realtime Data Mining
Edité par
Birkhäuser, Springer Dez 2013, 2013
ISBN 10 : 3319013203
ISBN 13 : 9783319013206
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783319013206
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