Editorial: Applications of Machine Learning to Evolutionary Ecology Data

Juliano Morimoto; Aurore Ponchon; Georgy Sofronov; Justin Travis

doi:10.3389/fevo.2021.797319

Editorial: Applications of Machine Learning to Evolutionary Ecology Data

Juliano Morimoto^*, Aurore Ponchon, Georgy Sofronov, Justin Travis

^*Corresponding author for this work

Aberdeen Centre For Environmental Sustainability

Macquarie University

Research output: Contribution to journal › Editorial › peer-review

Abstract

Machine Learning (ML) has become an increasingly popular tool in a large variety of fields due to an explosion of ways in which large-scale datasets are collected. ML has been used both as the means through which large data is generated (e.g., image recognition and classification) as well as the tools through which information and knowledge are acquired from large data through complex processing of high-dimensional data (James et al., 2013; Kroese et al., 2019).

Original language	English
Article number	797319
Journal	Frontiers in Ecology and Evolution
Volume	9
DOIs	https://doi.org/10.3389/fevo.2021.797319
Publication status	Published - 30 Nov 2021

Bibliographical note

Funding Information:
We acknowledge the authors that kindly supported our initiative and shared their work in this Research Topic.

Keywords

artificial intelligence
ecological theories
innovation
machine learning
niche adaptation

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10.3389/fevo.2021.797319Licence: CC BY

Cite this

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abstract = "Machine Learning (ML) has become an increasingly popular tool in a large variety of fields due to an explosion of ways in which large-scale datasets are collected. ML has been used both as the means through which large data is generated (e.g., image recognition and classification) as well as the tools through which information and knowledge are acquired from large data through complex processing of high-dimensional data (James et al., 2013; Kroese et al., 2019). ",

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AU - Morimoto, Juliano

AU - Ponchon, Aurore

AU - Sofronov, Georgy

AU - Travis, Justin

N1 - Funding Information: We acknowledge the authors that kindly supported our initiative and shared their work in this Research Topic.

PY - 2021/11/30

Y1 - 2021/11/30

N2 - Machine Learning (ML) has become an increasingly popular tool in a large variety of fields due to an explosion of ways in which large-scale datasets are collected. ML has been used both as the means through which large data is generated (e.g., image recognition and classification) as well as the tools through which information and knowledge are acquired from large data through complex processing of high-dimensional data (James et al., 2013; Kroese et al., 2019).

AB - Machine Learning (ML) has become an increasingly popular tool in a large variety of fields due to an explosion of ways in which large-scale datasets are collected. ML has been used both as the means through which large data is generated (e.g., image recognition and classification) as well as the tools through which information and knowledge are acquired from large data through complex processing of high-dimensional data (James et al., 2013; Kroese et al., 2019).

KW - artificial intelligence

KW - ecological theories

KW - innovation

KW - machine learning

KW - niche adaptation

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JO - Frontiers in Ecology and Evolution

JF - Frontiers in Ecology and Evolution

M1 - 797319

ER -

Editorial: Applications of Machine Learning to Evolutionary Ecology Data

Abstract

Bibliographical note

Keywords

Access to Document

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