Computational trust models and machine learning /

"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 var...

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Bibliographic Details
Corporate Author: Taylor & Francis
Other Authors: Liu, Xin (Mathematician) (Editor), Datta, Anwitaman (Editor), Lim, Ee-Peng (Editor)
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, [2015]
Series:Chapman & Hall/CRC machine learning & pattern recognition series.
Subjects:
Online Access:Connect to the full text of this electronic book
Connect to the full text of this electronic book
Description
Summary:"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"--
Item Description:"A Chapman & Hall book."
Physical Description:1 online resource (xxiv, 208 pages)
Bibliography:Includes bibliographical references and index.
ISBN:9781482226676
1482226677
9781322637464
1322637466