Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools
Dombi, József
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools by József Dombi, Orsolya Csiszár. - First edition 2021 - 1 recurso en línea (XXI, 173 páginas) 56 ilustraciones, 50 ilustraciones a color - Studies in Fuzziness and Soft Computing 408 1434-9922 Intelligent Technologies and Robotics (SpringerNature-42732) Intelligent Technologies and Robotics (R0) (SpringerNature-43728) .
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.
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.
9783030722807
10.1007/978-3-030-72280-7 doi
Aprendizaje automático
QA76.89 / 2021 EB
Explainable Neural Networks Based on Fuzzy Logic and Multi-criteria Decision Tools by József Dombi, Orsolya Csiszár. - First edition 2021 - 1 recurso en línea (XXI, 173 páginas) 56 ilustraciones, 50 ilustraciones a color - Studies in Fuzziness and Soft Computing 408 1434-9922 Intelligent Technologies and Robotics (SpringerNature-42732) Intelligent Technologies and Robotics (R0) (SpringerNature-43728) .
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.
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.
9783030722807
10.1007/978-3-030-72280-7 doi
Aprendizaje automático
QA76.89 / 2021 EB