| Description |
1 online resource (xxi, 173 pages : illustrations (some color)) |
| Series |
Studies in fuzziness and soft computing, 1434-9922 ; volume 408 |
|
Studies in fuzziness and soft computing ; v. 408. 1434-9922
|
| 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)
|
| Form |
Electronic book
|
| Author |
Csiszár, Orsolya, author
|
| ISBN |
9783030722807 |
|
3030722805 |
|
