9783030940683 - How Fuzzy Concepts Contribute to Machine Learning (Studies in Fuzziness and Soft Computing, Band 416) von Eftekhari, Mahdi; Mehrpooya, Adel; Saberi-Movahed, Farid; ... (6 Ergebnisse)

Zustand: New. In.

Paperback. Zustand: Brand New. 179 pages. 9.25x6.10x0.38 inches. In Stock.

Zustand: New.

Taschenbuch. Zustand: Neu. How Fuzzy Concepts Contribute to Machine Learning | Mahdi Eftekhari (u. a.) | Taschenbuch | Studies in Fuzziness and Soft Computing | xii | Englisch | 2023 | Springer | EAN 9783030940683 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces somecontemporaryapproacheson the application offuzzy and hesitant fuzzy sets in machine learning tasks such asclassification, clustering and dimensionreduction.Manysituationsarisein machine learning algorithmsinwhichapplying methods for uncertaintymodeling andmulti-criteriadecision making can lead toabetterunderstanding ofalgorithms behavior as well as achievinggood performances.Specifically,the present book is a collection of novel viewpointson howfuzzy andhesitant fuzzy conceptscan beappliedtodata uncertainty modeling aswell asbeing used to solvemulti-criteria decisionmaking challengesraised in machine learning problems. Using the multi-criteria decisionmaking framework, the book shows how different algorithms, rather thanhuman experts,areemployedto determine membership degrees. The book is expected to bring closerthe communities of pure mathematicians of fuzzysets and data scientists.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.