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001444473 001__ 1444473
001444473 003__ OCoLC
001444473 005__ 20230310003711.0
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001444473 019__ $$a1298200061$$a1298389004
001444473 020__ $$a9783030940669$$q(electronic bk.)
001444473 020__ $$a3030940667$$q(electronic bk.)
001444473 020__ $$z9783030940652
001444473 020__ $$z3030940659
001444473 0247_ $$a10.1007/978-3-030-94066-9$$2doi
001444473 035__ $$aSP(OCoLC)1298165351
001444473 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001444473 049__ $$aISEA
001444473 050_4 $$aQ325.5$$b.E48 2022
001444473 08204 $$a006.3/1015113223$$223
001444473 1001_ $$aEftekhari, Mahdi,$$eauthor.
001444473 24510 $$aHow fuzzy concepts contribute to machine learning /$$cMahdi Eftekhari, Adel Mehrpooya, Farid Saberi-Movahed, Vicenç Torra.
001444473 264_1 $$aCham :$$bSpringer,$$c[2022]
001444473 264_4 $$c©2022
001444473 300__ $$a1 online resource :$$bcolor illustrations.
001444473 336__ $$atext$$btxt$$2rdacontent
001444473 337__ $$acomputer$$bc$$2rdamedia
001444473 338__ $$aonline resource$$bcr$$2rdacarrier
001444473 4901_ $$aStudies in fuzziness and soft computing ;$$vvolume 416
001444473 504__ $$aIncludes bibliographical references.
001444473 5050_ $$aChapter 1: Preliminaries -- Chapter 2: A Denition for Hesitant Fuzzy Partitions -- Chapter 3: Unsupervised Feature Selection Method. Chapter 4: Fuzzy Partitioning of Continuous Attributes -- Chapter 5: Comparing Different Stopping Criteria.
001444473 506__ $$aAccess limited to authorized users.
001444473 520__ $$aThis 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. .
001444473 588__ $$aDescription based on print version record.
001444473 650_0 $$aMachine learning.
001444473 650_0 $$aFuzzy sets.
001444473 650_6 $$aApprentissage automatique.
001444473 650_6 $$aEnsembles flous.
001444473 655_0 $$aElectronic books.
001444473 7001_ $$aMehrpooya, Adel,$$eauthor.
001444473 7001_ $$aSaberi-Movahed, Farid,$$eauthor.
001444473 7001_ $$aTorra, Vicenç,$$eauthor.
001444473 77608 $$iPrint version:$$aEftekhari, Mahdi.$$tHow fuzzy concepts contribute to machine learning.$$dCham : Springer, 2022$$z9783030940652$$w(OCoLC)1295193452
001444473 830_0 $$aStudies in fuzziness and soft computing ;$$vv. 416.
001444473 852__ $$bebk
001444473 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-94066-9$$zOnline Access$$91397441.1
001444473 909CO $$ooai:library.usi.edu:1444473$$pGLOBAL_SET
001444473 980__ $$aBIB
001444473 980__ $$aEBOOK
001444473 982__ $$aEbook
001444473 983__ $$aOnline
001444473 994__ $$a92$$bISE