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

Contributor(s): Series: Chapman & Hall/CRC machine learning & pattern recognition seriesPublisher: Boca Raton : Taylor & Francis, 2015Copyright date: ©2015Description: xxiv, 208 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781482226669
Subject(s): 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'-- Provided by publisher.
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Item type Current library Home library Collection Call number Materials specified Copy number Status Date due Barcode
AM PERPUSTAKAAN LINGKUNGAN KEDUA PERPUSTAKAAN LINGKUNGAN KEDUA KOLEKSI AM-P. LINGKUNGAN KEDUA - Q342.C64 3 (Browse shelf(Opens below)) 1 Available 00002188262

Includes bibliographical references and index.

'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.

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