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Sampling techniques for supervised o...

Ros, Frederic.

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Sampling techniques for supervised or unsupervised tasks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sampling techniques for supervised or unsupervised tasks/ edited by Frederic Ros, Serge Guillaume.
other author:	Ros, Frederic.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiii, 232 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction 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.
Contained By:	Springer eBooks
Subject:	Sampling (Statistics) -
Online resource:	https://doi.org/10.1007/978-3-030-29349-9
ISBN:	9783030293499

Sampling techniques for supervised or unsupervised tasks
Sampling techniques for supervised or unsupervised tasks[electronic resource] /edited by Frederic Ros, Serge Guillaume. - Cham :Springer International Publishing :2020. - xiii, 232 p. :ill. (some col.), digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

Introduction 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.

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.

ISBN: 9783030293499

Standard No.: 10.1007/978-3-030-29349-9doiSubjects--Topical Terms:

545623
Sampling (Statistics)

LC Class. No.: QA276.6

Dewey Class. No.: 519.52

Sampling techniques for supervised or unsupervised tasks
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245 0 0 $a Sampling techniques for supervised or unsupervised tasks $h [electronic resource] / $c edited by Frederic Ros, Serge Guillaume.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 232 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-848X
505 0 $a Introduction 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 $a 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.
650 0 $a Sampling (Statistics) $3 545623
650 0 $a Algorithms. $3 536374
650 0 $a Computational intelligence. $3 595739
650 0 $a Data mining. $3 562972
650 0 $a Big data. $3 2045508
650 0 $a Pattern perception. $3 649387
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Ros, Frederic. $3 3444431
700 1 $a Guillaume, Serge. $3 3444432
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Unsupervised and semi-supervised learning. $3 3380848
856 4 0 $u https://doi.org/10.1007/978-3-030-29349-9
950 $a Engineering (Springer-11647)