Computational trust models and machine learning / editors Xin Liu, EPFL, Lausanne, Switzerland, Anwitaman Datta, Nanyang Technological University, Singapore, Ee-Peng Lim, Singapore Management University.

Other author Liu, Xin (Mathematician)
Other author Datta, Anwitaman.
Other author Lim, Ee-Peng.
Format Electronic
Publication InfoBoca Raton : CRC Press/Taylor & Francis Group, 2014.
Descriptionxxiv, 208 pages : illustrations, charts ; 24 cm.
Supplemental ContentFull text available from Ebook Central - Academic Complete
Subjects
Computational intelligence.
Machine learning.
Truthfulness and falsehood--Mathematical models.

SeriesChapman & Hall/CRC machine learning & pattern recognition series
Abstract "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"-- Provided by publisher.
Bibliography noteIncludes bibliographical references (pages 175-201) and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2014028238
ISBN9781482226669 (hardback)

Availability

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Electronic Resources Access Content Online ✔ Available