Language: English
Published by Morgan & Claypool Publishers, 2014
ISBN 10: 1627052577 ISBN 13: 9781627052573
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Language: Italian
Published by CLEUP (15 ottobre 2015), 2015
ISBN 10: 8867874691 ISBN 13: 9788867874699
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Language: Italian
Published by CLEUP (13 dicembre 2013), 2013
ISBN 10: 886787165X ISBN 13: 9788867871650
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Published by Commission canadienne pour l 'Unesco,, Paris,, 1990
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in-8, Broché, 335 pages. Bon état.
Language: Italian
Published by CLEUP (20 ottobre 2013), 2013
ISBN 10: 8867871447 ISBN 13: 9788867871445
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Published by Idea Montagna Edizioni, 2016
ISBN 10: 8897299903 ISBN 13: 9788897299905
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Language: Italian
Published by CLEUP (15 ottobre 2015), 2015
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Brossura. Condition: new. A cura di Cappellari F.Teolo, 2016; br., pp. 320, ill. col., tavv. col., cm 15x21.(Rock & Ice. 6). Prosegue e si conclude con questo volume la descrizione degli itinerari di neve e ghiaccio esistenti sul crinale appenninico tra Emilia Romagna e Toscana. L'opera prende in considerazioni le montagne più a est e comprende i gruppi Giovo-Rondinaio, Alpe delle Tre Potenze, Monte Gomito, Monte Cimone, Libro Aperto, Monte Spigolino, Monte Corno alle Scale. Libro.
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Language: Italian
Published by CLEUP Cooperativa Libraria Editrice Università di Padova, Padova, 2001
ISBN 10: 8871785177 ISBN 13: 9788871785172
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First Edition
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Language: English
Published by Springer International Publishing, Springer International Publishing Mai 2014, 2014
ISBN 10: 3031007786 ISBN 13: 9783031007781
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Taschenbuch. Condition: Neu. Neuware -The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 200 pp. Englisch.
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Published by Springer International Publishing, 2014
ISBN 10: 3031007786 ISBN 13: 9783031007781
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
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Taschenbuch. Condition: Neu. Probabilistic Approaches to Recommendations | Nicola Barbieri (u. a.) | Taschenbuch | Synthesis Lectures on Data Mining and Knowledge Discovery | xv | Englisch | 2014 | Springer | EAN 9783031007781 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Published by cleup, 2001
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Reggio Emilia, Amministrazione Comunale, 2006, 8vo brossura con copertina illustrata, pp. 197 (supplemento a Reggio Comune, n. 4) .
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Published by Hachette Livre - BNF Mär 2018, 2018
ISBN 10: 2019238373 ISBN 13: 9782019238377
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