Standard

Supervised Classification : Quite a Brief Overview. / Loog, Marco.

Machine Learning Techniques for Space Weather. ed. / E. Camporeale; S. Wing; J.R. Johnson. Elsevier, 2018. p. 113-145.

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

Harvard

Loog, M 2018, Supervised Classification: Quite a Brief Overview. in E Camporeale, S Wing & JR Johnson (eds), Machine Learning Techniques for Space Weather. Elsevier, pp. 113-145. https://doi.org/10.1016/B978-0-12-811788-0.00005-6

APA

Loog, M. (2018). Supervised Classification: Quite a Brief Overview. In E. Camporeale, S. Wing, & J. R. Johnson (Eds.), Machine Learning Techniques for Space Weather (pp. 113-145). Elsevier. https://doi.org/10.1016/B978-0-12-811788-0.00005-6

Vancouver

Loog M. Supervised Classification: Quite a Brief Overview. In Camporeale E, Wing S, Johnson JR, editors, Machine Learning Techniques for Space Weather. Elsevier. 2018. p. 113-145 https://doi.org/10.1016/B978-0-12-811788-0.00005-6

Author

Loog, Marco. / Supervised Classification : Quite a Brief Overview. Machine Learning Techniques for Space Weather. editor / E. Camporeale ; S. Wing ; J.R. Johnson. Elsevier, 2018. pp. 113-145

BibTeX

@inbook{a78ed7662b2a469994733fbfb18d6d5e,
title = "Supervised Classification: Quite a Brief Overview",
abstract = "The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements—also called features or inputs—to the so-called class label—or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to approach a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.",
keywords = "Supervised learning, Pattern recognition, Machine learning, Representation, Classification, Evaluation",
author = "Marco Loog",
year = "2018",
doi = "10.1016/B978-0-12-811788-0.00005-6",
language = "English",
isbn = "978-0-12-811788-0",
pages = "113--145",
editor = "E. Camporeale and S. Wing and J.R. Johnson",
booktitle = "Machine Learning Techniques for Space Weather",
publisher = "Elsevier",

}

RIS

TY - CHAP

T1 - Supervised Classification

T2 - Quite a Brief Overview

AU - Loog, Marco

PY - 2018

Y1 - 2018

N2 - The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements—also called features or inputs—to the so-called class label—or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to approach a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.

AB - The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements—also called features or inputs—to the so-called class label—or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to approach a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.

KW - Supervised learning

KW - Pattern recognition

KW - Machine learning

KW - Representation

KW - Classification

KW - Evaluation

U2 - 10.1016/B978-0-12-811788-0.00005-6

DO - 10.1016/B978-0-12-811788-0.00005-6

M3 - Chapter

SN - 978-0-12-811788-0

SP - 113

EP - 145

BT - Machine Learning Techniques for Space Weather

A2 - Camporeale, E.

A2 - Wing, S.

A2 - Johnson, J.R.

PB - Elsevier

ER -

ID: 47688426