Algorithms in machine learning paradigms /

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Bibliographic Details
Imprint:Singapore : Springer, 2020.
Description:1 online resource (x, 195 pages) : illustrations (some color)
Language:English
Series:Studies in computational intelligence, 1860-949X ; volume 870
Studies in computational intelligence ; 870.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12603210
Hidden Bibliographic Details
Other authors / contributors:Mandal, Jyotsna Kumar, 1960- editor.
Mukhopadhyay, Somnath, 1983- editor.
Dutta, Paramartha, editor.
Dasgupta, Kousik, editor.
ISBN:9789811510410
9811510415
9789811510403
9811510407
Notes:Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed January 16, 2020).
Summary:This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning, statistical rule-based learning, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence-based learning.
Other form:Print version: Algorithms in machine learning paradigms. Singapore : Springer, 2020 9811510407 9789811510403
Standard no.:10.1007/978-981-15-1041-0