Computational trust models and machine learning - Info and Reading Options
By Liu, Xin (Mathematician), Anwitaman Datta and Ee-Peng Lim
"Computational trust models and machine learning" was published by Taylor & Francis in 2014 - Boca Raton, it has 208 pages and the language of the book is English.
“Computational trust models and machine learning” Metadata:
- Title: ➤ Computational trust models and machine learning
- Authors: Liu, Xin (Mathematician)Anwitaman DattaEe-Peng Lim
- Language: English
- Number of Pages: 208
- Publisher: Taylor & Francis
- Publish Date: 2014
- Publish Location: Boca Raton
“Computational trust models and machine learning” Subjects and Themes:
- Subjects: ➤ Computational intelligence - Machine learning - Truthfulness and falsehood - Mathematical models - COMPUTERS / Machine Theory - COMPUTERS / Software Development & Engineering / Systems Analysis & Design - TECHNOLOGY & ENGINEERING / Electronics / General - Intelligence informatique - Apprentissage automatique - Mensonge - Modèles mathématiques - COMPUTERS - General
Edition Specifications:
- Pagination: pages cm
Edition Identifiers:
- The Open Library ID: OL31034274M - OL23200294W
- Online Computer Library Center (OCLC) ID: 894169847 - 884440170
- Library of Congress Control Number (LCCN): 2014028238
- ISBN-13: 9781482226669
- All ISBNs: 9781482226669
AI-generated Review of “Computational trust models and machine learning”:
"Computational trust models and machine learning" Description:
The Open Library:
"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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