Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

Anton Akusok; Leonardo Espinosa Leal; Kaj-Mikael Björk; Amaury Lendasse

doi:10.1007/978-3-030-58989-9_8

Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

Anton Akusok^*, Leonardo Espinosa Leal, Kaj-Mikael Björk, Amaury Lendasse

^*Corresponding author for this work

Finance, Helsinki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM (https://github.com/akusok/scikit-elm, https://scikit-elm.readthedocs.io). Usability and flexibility of the approach are the main focus points in this work, achieved primarily through a tight integration with Scikit-Learn, a de facto industry standard library in Machine Learning outside Deep Learning. Methodological advances enable great flexibility in dynamic addition of new classes to a trained model, or by allowing a model to forget previously learned data.

Original language	English
Title of host publication	Proceedings of ELM2019
Editors	Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Place of Publication	Cham
Publisher	Springer
Publication date	2021
Pages	69-78
ISBN (Print)	978-3-030-58988-2
ISBN (Electronic)	978-3-030-58989-9
DOIs	https://doi.org/10.1007/978-3-030-58989-9_8
Publication status	Published - 2021
MoE publication type	A4 Article in conference proceedings

Publication series

Name	Proceedings in Adaptation, Learning and Optimization
Volume	14
ISSN (Print)	2363-6084
ISSN (Electronic)	2363-6092

Keywords

113 Computer and information sciences

Access to Document

10.1007/978-3-030-58989-9_8

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title = "Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning",

abstract = "This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM (https://github.com/akusok/scikit-elm, https://scikit-elm.readthedocs.io). Usability and flexibility of the approach are the main focus points in this work, achieved primarily through a tight integration with Scikit-Learn, a de facto industry standard library in Machine Learning outside Deep Learning. Methodological advances enable great flexibility in dynamic addition of new classes to a trained model, or by allowing a model to forget previously learned data. ",

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author = "Anton Akusok and Leal, {Leonardo Espinosa} and Kaj-Mikael Bj{\"o}rk and Amaury Lendasse",

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language = "English",

isbn = "978-3-030-58988-2",

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pages = "69--78",

editor = "Jiuwen Cao and Vong, {Chi Man} and Yoan Miche and Amaury Lendasse",

booktitle = "Proceedings of ELM2019",

address = "International",

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Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning. / Akusok, Anton; Leal, Leonardo Espinosa; Björk, Kaj-Mikael et al.
Proceedings of ELM2019. ed. / Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lendasse. Cham: Springer, 2021. p. 69-78 (Proceedings in Adaptation, Learning and Optimization; Vol. 14).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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AB - This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM (https://github.com/akusok/scikit-elm, https://scikit-elm.readthedocs.io). Usability and flexibility of the approach are the main focus points in this work, achieved primarily through a tight integration with Scikit-Learn, a de facto industry standard library in Machine Learning outside Deep Learning. Methodological advances enable great flexibility in dynamic addition of new classes to a trained model, or by allowing a model to forget previously learned data.

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Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

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