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Record Nr. |
UNINA9910787962603321 |
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Titolo |
Computational trust models and machine learning / / edited by Xin Liu, EPFL, Lausanne, Switzerland, Anwitaman Datta, Nanyang Technological University, Singapore, Ee-Peng Lim, Singapore Management University |
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Pubbl/distr/stampa |
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Boca Raton : , : Taylor & Francis, , [2015] |
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©2015 |
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ISBN |
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0-429-15948-X |
1-4822-2667-7 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (227 p.) |
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Collana |
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Chapman & Hall/CRC machine learning & pattern recognition series |
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Classificazione |
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COM037000COM051240TEC008000 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Truthfulness and falsehood - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Front Cover; Series Page; Dedication; Contents; List of Figures; Preface; About the Editors; Contributors; Chapter 1: Introduction; Chapter 2: Trust in Online Communities; Chapter 3: Judging the Veracity of Claims and Reliability of Sources with Fact-Finders; Chapter 4: Web Credibility Assessment; Chapter 5: Trust-Aware Recommender Systems; Chapter 6: Biases in Trust-Based Systems; Bibliography; Back Cover |
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Sommario/riassunto |
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This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches-- |
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