Explainable neural networks based on fuzzy logic and multi-criteria decision tools /

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Bibliographic Details
Author / Creator:	Dombi, József, author.
Imprint:	Cham, Switzerland : Springer, [2021]
Description:	1 online resource (xxi, 173 pages) : illustrations (some color).
Language:	English
Series:	Studies in fuzziness and soft computing, 1434-9922 ; volume 408 Studies in fuzziness and soft computing ; v. 408.
Subject:	Neural networks (Computer science) Fuzzy logic. Machine learning. Artificial intelligence. Artificial intelligence. Fuzzy logic. Machine learning. Neural networks (Computer science) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12612691

Hidden Bibliographic Details
Other authors / contributors:	Csiszár, Orsolya, author.
ISBN:	9783030722807 3030722805 3030722791 9783030722791
Notes:	Includes bibliographical references. Online resource; title from PDF title page (SpringerLink, viewed May 7, 2021).
Summary:	The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable -- and even, in many cases, more efficient. Fuzzy logic together with multi-criteria decision-making tools provides very powerful tools for modeling human thinking. Based on their common theoretical basis, we propose a consistent framework for modeling human thinking by using the tools of all three fields: fuzzy logic, multi-criteria decision-making, and deep learning to help reduce the black-box nature of neural models; a challenge that is of vital importance to the whole research community.
Other form:	Print version: 9783030722791
Standard no.:	10.1007/978-3-030-72280-7

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