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Computational trust models and machine learning / editors Xin Liu; Anwitaman Datta and Ee-Peng Lim,

Contributor(s): Liu, Xin | Datta, Anwitaman | Lim, Ee-Peng | (Mathematician)Material type: TextTextLanguage: English Series: Chapman & Hall/CRC machine learning & pattern recognition seriesPublication details: Boca Raton, FL : CRC Press, [2015] ©2015 Description: xxiv, 208 pages : illustrations, charts ; 24 cmISBN: 9781482226669 (hardback)Subject(s): Computational intelligence | Machine learning | Truthfulness and falsehood | COMPUTERS / Machine Theory | COMPUTERS / Software Development & Engineering / Systems Analysis & Design | TECHNOLOGY & ENGINEERING / Electronics / General | -- Mathematical models | | | DDC classification: 006.31 Other classification: COM037000 | COM051240 | TEC008000
Contents:
1. Introduction -- 2. Trust in online communities -- 3. Judging the veracity of claims and reliability of sources -- 4. Web credibility assessment -- 5. Trust-aware recommender systems -- 6. Biases in trust-based systems.
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"--
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Item type Current library Collection Call number Status Date due Barcode
General Books General Books CUTN Central Library

This is a searchable open catalogue of all library of the Central University of Tamil Nadu.

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Non-fiction 006.31 LIU (Browse shelf(Opens below)) Available 36779

1. Introduction --
2. Trust in online communities --
3. Judging the veracity of claims and reliability of sources --
4. Web credibility assessment --
5. Trust-aware recommender systems --
6. Biases in trust-based systems.

"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"--

Includes bibliographical references (pages 175-201) and index.

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