| Description |
1 online resource (227 pages) : illustrations. |
|
text rdacontent |
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computer rdamedia |
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online resource rdacarrier |
| Series |
Chapman & Hall/CRC machine learning & pattern recognition series |
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Chapman & Hall/CRC machine learning & pattern recognition series.
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| Bibliography |
Includes bibliographical references and index. |
| 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. |
| Note |
Description based on print version record. |
| Subject |
Computational intelligence.
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Machine learning.
|
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Truthfulness and falsehood -- Mathematical models.
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| Genre/Form |
Electronic books.
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| Added Author |
Liu, Xin (Mathematician), editor.
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Datta, Anwitaman, editor.
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Lim, Ee-Peng, editor.
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| Other Form: |
Print version: Computational trust models and machine learning. Boca Raton : Taylor & Francis, [2015] Chapman & Hall/CRC machine learning & pattern recognition series 9781482226669 (DLC)10961793 |
| ISBN |
9781482226669 (hardback) |
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9781482226676 (e-book) |
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