Sampling techniques for supervised or unsupervised tasks /

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Bibliographic Details
Imprint:Cham, Switzerland : Springer, [2020]
Description:1 online resource (xiii, 232 pages) : illustrations (some color).
Language:English
Series:Unsupervised and semi-supervised learning, 2522-8498
Unsupervised and semi-supervised learning.
Subject:Sampling (Statistics)
Algorithms.
Computational intelligence.
Data mining.
Big data.
Pattern perception.
Algorithms.
Big data.
Computational intelligence.
Data mining.
Pattern perception.
Sampling (Statistics)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12602343
Hidden Bibliographic Details
Other authors / contributors:Ros, Frédéric, editor.
Guillaume, Serge, editor.
ISBN:9783030293499
3030293491
9783030293482
Notes:Includes bibliographical references and index.
Online resource; title from PDF title page (SpringerLink, viewed October 30, 2019).
Summary: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.
Standard no.:10.1007/978-3-030-29349-9