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000922619 019__ $$a1119473572$$a1121274034
000922619 020__ $$a9783030224752$$q(electronic book)
000922619 020__ $$a3030224759$$q(electronic book)
000922619 020__ $$z3030224740
000922619 020__ $$z9783030224745
000922619 0248_ $$a10.1007/978-3-030-22
000922619 035__ $$aSP(OCoLC)on1119641978
000922619 035__ $$aSP(OCoLC)1119641978$$z(OCoLC)1119473572$$z(OCoLC)1121274034
000922619 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dYDX$$dGW5XE$$dLQU$$dOCLCF$$dOCLCQ
000922619 049__ $$aISEA
000922619 050_4 $$aQ325.5
000922619 08204 $$a006.31$$223
000922619 24500 $$aSupervised and unsupervised learning for data science /$$cMichael W. Berry, Azlinah Mohamed, Bee Wah Yap.
000922619 260__ $$aCham :$$bSpringer,$$c2020.
000922619 300__ $$a1 online resource (191 pages)
000922619 336__ $$atext$$btxt$$2rdacontent
000922619 337__ $$acomputer$$bc$$2rdamedia
000922619 338__ $$aonline resource$$bcr$$2rdacarrier
000922619 4901_ $$aUnsupervised and semi-supervised learning,$$x2522-8498
000922619 504__ $$aIncludes bibliographical references and index.
000922619 506__ $$aAccess limited to authorized users.
000922619 520__ $$aThis book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
000922619 588__ $$aDescription based on print version record.
000922619 650_0 $$aMachine learning.
000922619 650_0 $$aSupervised learning (Machine learning)
000922619 7001_ $$aBerry, Michael W.
000922619 7001_ $$aMohamed, Azlinah Hj.
000922619 7001_ $$aWah, Yap Bee.
000922619 77608 $$iPrint version:$$aBerry, Michael W.$$tSupervised and Unsupervised Learning for Data Science.$$dCham : Springer, ©2019$$z9783030224745
000922619 830_0 $$aUnsupervised and semi-supervised learning.
000922619 852__ $$bebk
000922619 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-22475-2$$zOnline Access$$91397441.1
000922619 909CO $$ooai:library.usi.edu:922619$$pGLOBAL_SET
000922619 980__ $$aEBOOK
000922619 980__ $$aBIB
000922619 982__ $$aEbook
000922619 983__ $$aOnline
000922619 994__ $$a92$$bISE