ISBN: 9783319013206 - realtime data mining: self-learning techniques for recommendation engines (10 risultati)

Condizione: 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.…

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: 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. . . . .…

Buch. Condizione: 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.…

Hardcover. Condizione: Brand New. 290 pages. 9.25x6.50x1.00 inches. In Stock.

Condizione: 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. . . . . Books ship from the US and Ireland.…

Buch. Condizione: 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.…

Gebunden. Condizione: 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.…

Buch. Condizione: 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.…