Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools: 408 - Rilegato

Dombi, Jo“zsef; Csiszįr, Orsolya

Rilegato

ISBN 10: 3030722791 ISBN 13: 9783030722791

Casa editrice: Springer Nature, 2021

Riassunto

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.

Su questo libro

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030722791
• ISBN 13: 9783030722791
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 196
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com