Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools: Studies in Fuzziness and Soft Computing, cartea 408
Autor József Dombi, Orsolya Csiszáren Limba Engleză Paperback – 29 apr 2022
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.
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 943.89 lei 6-8 săpt. | |
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| Springer International Publishing – 29 apr 2021 | 949.94 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783030722821
ISBN-10: 3030722821
Pagini: 173
Ilustrații: XXI, 173 p. 56 illus., 50 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Cham, Switzerland
ISBN-10: 3030722821
Pagini: 173
Ilustrații: XXI, 173 p. 56 illus., 50 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.28 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Fuzziness and Soft Computing
Locul publicării:Cham, Switzerland
Cuprins
Chapter 1: Connectives: Conjunctions, Disjunctions and Negations.- Chapter 2: Implications.- Chapter 3: Equivalences.- Chapter 4: Modifiers and Membership Functions in Fuzzy Sets.- Chapter 5: Aggregative Operators.- Chapter 6: Preference Operators.
Textul de pe ultima copertă
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.
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.
Caracteristici
Presents the current state-of-the-art in Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools Presents recent research focusing on a special class of continuous-valued logic and multi-criteria decision tools Proposes a consistent framework for modeling human thinking by using the tools of both fields: fuzzy logical operators as well as multi-criteria decision tools, such as aggregative and preference operators