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001 978-3-030-29349-9
003 DE-He213
005 20220801214104.0
007 cr nn 008mamaa
008 191026s2020 sz | s |||| 0|eng d
020 _a9783030293499
_9978-3-030-29349-9
024 7 _a10.1007/978-3-030-29349-9
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
245 1 0 _aSampling Techniques for Supervised or Unsupervised Tasks
_h[electronic resource] /
_cedited by Frédéric Ros, Serge Guillaume.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXIII, 232 p. 40 illus., 30 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
505 0 _aIntroduction to sampling techniques -- Core-sets: an Updated Survey -- A family of unsupervised sampling algorithms -- From supervised instance and feature selection algorithms to dual selection: A Review -- Approximating Spectral Clustering via Sampling: A Review -- Sampling technique for complex data -- Boosting the Exploration of Huge Dynamic Graphs.
520 _aThis book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability and the “curse of dimensionality”, their implementations, evaluations, and applications. A large part of the book is dedicated to database comprising standard feature vectors, and a special section is reserved to the handling of more complex objects and dynamic scenarios. The book is ideal for anyone teaching or learning pattern recognition and interesting teaching or learning pattern recognition and is interested in the big data challenge. It provides an accessible introduction to the field and discusses the state of the art concerning sampling techniques for supervised and unsupervised task. Provides a comprehensive description of sampling techniques for unsupervised and supervised tasks; Describe implementation and evaluation of algorithms that simultaneously manage scalable problems and curse of dimensionality; Addresses the role of sampling in dynamic scenarios, sampling when dealing with complex objects, and new challenges arising from big data. "This book represents a timely collection of state-of-the art research of sampling techniques, suitable for anyone who wants to become more familiar with these helpful techniques for tackling the big data challenge." M. Emre Celebi, Ph.D., Professor and Chair, Department of Computer Science, University of Central Arkansas "In science the difficulty is not to have ideas, but it is to make them work" From Carlo Rovelli.
650 0 _aTelecommunication.
_910437
650 0 _aComputational intelligence.
_97716
650 0 _aData mining.
_93907
650 0 _aQuantitative research.
_94633
650 0 _aPattern recognition systems.
_93953
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aData Mining and Knowledge Discovery.
_936231
650 2 4 _aData Analysis and Big Data.
_936232
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aRos, Frédéric.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_936233
700 1 _aGuillaume, Serge.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_936234
710 2 _aSpringerLink (Online service)
_936235
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030293482
776 0 8 _iPrinted edition:
_z9783030293505
776 0 8 _iPrinted edition:
_z9783030293512
830 0 _aUnsupervised and Semi-Supervised Learning,
_x2522-8498
_936236
856 4 0 _uhttps://doi.org/10.1007/978-3-030-29349-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75943
_d75943