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Publicado por Springer-Verlag Gmbh, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: AHA-BUCH, Einbeck, Alemania
Libro
Gebundene Ausgabe. Condición: Gebraucht. 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.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: GF Books, Inc., Hawthorne, CA, Estados Unidos de America
Libro
Condición: Fine. Book is in Used-LikeNew condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: GF Books, Inc., Hawthorne, CA, Estados Unidos de America
Libro
Condición: New. Book is in NEW condition.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Book Deals, Tucson, AZ, Estados Unidos de America
Libro
Condición: New. New! This book is in the same immaculate condition as when it was published.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
Libro
Condición: 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.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: GF Books, Inc., Hawthorne, CA, Estados Unidos de America
Libro
Condición: Very Good. Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Books Unplugged, Amherst, NY, Estados Unidos de America
Libro
Condición: Good. Buy with confidence! Book is in good condition with minor wear to the pages, binding, and minor marks within.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Book Deals, Tucson, AZ, Estados Unidos de America
Libro
Condición: Very Good. Very Good condition. Shows only minor signs of wear, and very minimal markings inside (if any).
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Book Deals, Tucson, AZ, Estados Unidos de America
Libro
Condición: Fine. Like New condition. Great condition, but not exactly fully crisp. The book may have been opened and read, but there are no defects to the book, jacket or pages.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: SMASS Sellers, IRVING, TX, Estados Unidos de America
Libro
Condición: 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.
Publicado por Birkhäuser, 2014
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: booksXpress, Bayonne, NJ, Estados Unidos de America
Libro
Hardcover. Condición: new.
Publicado por Birkhäuser, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Libro Impresión bajo demanda
Condición: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Publicado por Springer International Publishing Dez 2013, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Libro Impresión bajo demanda
Buch. Condición: 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.
Publicado por Springer International Publishing, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: moluna, Greven, Alemania
Libro Impresión bajo demanda
Gebunden. Condición: 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.
Publicado por Springer International Publishing, 2013
ISBN 10: 3319013203ISBN 13: 9783319013206
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro
Buch. Condición: 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.