How Fuzzy Concepts Contribute to Machine Learning (Studies in Fuzziness and Soft Computing) von Eftekhari, Mahdi; Mehrpooya, Adel; Saberi-Movahed, Farid; Torra, Vicen�: New (2023)

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Bibliografische Details

Titel: How Fuzzy Concepts Contribute to Machine Learning (Studies in Fuzziness and Soft Computing)
Verlag: Springer
Ver�ffentlichungsdatum: 2023
Sprache: Englisch
ISBN-10: 3030940683
ISBN-13: 9783030940683
Bindung: Softcover
Zustand: New

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Inhaltsangabe

This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the communities of pure mathematicians of fuzzy sets and data scientists.

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This book introduces some contemporary approaches on the application of fuzzy and hesitant fuzzy sets in machine learning tasks such as classification, clustering and dimension reduction. Many situations arise in machine learning algorithms in which applying methods for uncertainty modeling and multi-criteria decision making can lead to a better understanding of algorithms behavior as well as achieving good performances. Specifically, the present book is a collection of novel viewpoints on how fuzzy and hesitant fuzzy concepts can be applied to data uncertainty modeling as well as being used to solve multi-criteria decision making challenges raised in machine learning problems. Using the multi-criteria decision making framework, the book shows how different algorithms, rather than human experts, are employed to determine membership degrees. The book is expected to bring closer the communities of pure mathematicians of fuzzy sets and data scientists.

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