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020 _a9783030293499
024 7 _a10.1007/978-3-030-29349-9
_2doi
040 _aES-MaUEC
_bspa
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041 0 _aeng
050 4 _aQA276.6
_b2020 EB
245 0 0 _aSampling Techniques for Supervised or Unsupervised Tasks
_cedited by Frédéric Ros, Serge Guillaume
250 _aFirst edition
264 1 _aCham
_bSpringer International Publishing :
_bImprint Springer
_c2020
300 _a1 recurso en línea (XIII, 232 páginas)
_b 40 ilustraciones, 30 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aUnsupervised and Semi-Supervised Learning
_x2522-848X
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 3 _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.
988 _aPrimersemestre_2020_Engineering
650 7 _2embne
_9138933
_aMuestreo (Estadística)
700 1 _aRos, Frédéric
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGuillaume, Serge
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783030293482
776 0 8 _iPrinted edition:
_z9783030293505
776 0 8 _iPrinted edition:
_z9783030293512
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-29349-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b03/2020
_dz
_eb
_zSI