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, the book is classified in Computers genre, 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
- Is Family Friendly: Yes - No Mature Content
- Publisher: Taylor & Francis
- Publish Date: 2014
- Publish Location: Boca Raton
- Genres: Computers
“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:
- Google Books ID: bxQeBQAAQBAJ
- The Open Library ID: OL31034274M - OL23200294W
- Online Computer Library Center (OCLC) ID: 894169847 - 884440170
- Library of Congress Control Number (LCCN): 2014028238
- ISBN-13: 9781482226669
- ISBN-10: 1482226669
- All ISBNs: 9781482226669 - 1482226669
AI-generated Review of “Computational Trust Models and Machine Learning”:
Snippets and Summary:
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 ...
"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"--
Google Books:
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.
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- Public Domain: No
- Availability Status: Partially available
- Availability Status for country: US.
- Available Formats: Text is not avialbe, image copy is available.
- Google Books Link: Google Books
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- Is Online Borrowing Available: Yes
- Preview Status: restricted
- Check if available: The Open Library & The Internet Archive
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