Description |
1 online resource (xxi, 173 pages : illustrations (some color)) |
Series |
Studies in fuzziness and soft computing, 1434-9922 ; volume 408 |
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Studies in fuzziness and soft computing ; v. 408. 1434-9922
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Contents |
Part I. Elements of Nilpotent Fuzzy Logic -- Connectives: Conjunctions, Disjunctions and Negations -- Implications -- Equivalences -- Modifiers and Membership Functions in Fuzzy Sets -- Part II. Decision Operators -- Aggregative Operators -- Preference Operators -- Part III. Learning and Neural Networks -- Squashing Functions -- Learning Rules -- Interpretable Neural Networks Based on Continuous-Valued Logic and Multi-criteria Decision Operators -- Conclusions |
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 |
Bibliography |
Includes bibliographical references |
Notes |
Online resource; title from PDF title page (SpringerLink, viewed May 7, 2021) |
Subject |
Neural networks (Computer science)
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Fuzzy logic.
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Machine learning.
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Artificial intelligence.
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Neural Networks, Computer
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Artificial Intelligence
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Machine Learning
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artificial intelligence.
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Artificial intelligence
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Fuzzy logic
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Machine learning
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Neural networks (Computer science)
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Form |
Electronic book
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Author |
Csiszár, Orsolya, author
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ISBN |
9783030722807 |
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3030722805 |
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