Sampling Techniques for Supervised or Unsupervised Tasks

  • Frédéric Ros
  • Serge Guillaume

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Guillaume Chauvet
    Pages 1-21
  3. Dan Feldman
    Pages 23-44
  4. Serge Guillaume, Frédéric Ros
    Pages 45-81
  5. Nicolas Tremblay, Andreas Loukas
    Pages 129-183
  6. A. Idarrou, H. Douzi
    Pages 185-203
  7. F. Javier Calle, Dolores Cuadra, Jesica Rivero, Pedro Isasi
    Pages 205-226
  8. Back Matter
    Pages 227-232

About this book


This 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


Sampling techniques and algorithms Sampling techniques for unsupervised cases Unsupervised feature selection Scalability for unsupervised cases Sampling techniques for supervised cases Sampling algorithms for complex data Sampling algorithms for data streams

Editors and affiliations

  • Frédéric Ros
    • 1
  • Serge Guillaume
    • 2
  1. 1.PRISME LaboratoryUniversity of OrléansOrléansFrance
  2. 2.UMR ITAPIrsteaMontpellierFrance

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-030-29348-2
  • Online ISBN 978-3-030-29349-9
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site
Industry Sectors
IT & Software
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences