How Fuzzy Concepts Contribute to Machine Learning.

Eftekhari, Mahdi

ISBN 10: 3030940659 / ISBN 13: 9783030940652

Editore: Cham, Springer International Publishing., 2022

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1st ed. 2022. XII, 167 p. Hardcover. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Fuzziness and Soft Computing, 416. Sprache: Englisch.

Codice articolo 13245DB

Dati bibliografici

• Titolo: How Fuzzy Concepts Contribute to Machine Learning.
• Editore: Cham, Springer International Publishing.
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3030940659
• ISBN 13: 9783030940652
• Rilegatura: Rilegato
• Edizione: prima edizione
• Cataloghi dei venditori:
  • Informatik

Descrizione articolo

Riassunto

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

Dalla quarta di copertina

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.