Computational Trust Models and Machine Learning (Paperback)

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources-one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

General

Imprint:	Crc Press
Country of origin:	United Kingdom
Series:	Chapman & Hall/CRC Machine Learning & Pattern Recognition
Release date:	December 2020
First published:	2015
Editors:	Xin Liu • Anwitaman Datta • Ee-Peng Lim
Dimensions:	234 x 156mm (L x W)
Format:	Paperback
Pages:	232
ISBN-13:	978-0-367-73933-1
Categories:	Books > Professional & Technical > Environmental engineering & technology > General Books > Computing & IT > General theory of computing > General Books > Professional & Technical > Energy technology & engineering > Electrical engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	0-367-73933-X
Barcode:	9780367739331

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