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

Leonardo Espinosa-Leal; Anton Akusok; 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

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

^*Corresponding author for this work

Not published at VTT

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

Abstract

This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM. 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
Pages	69-78
ISBN (Electronic)	978-3-030-58989-9
ISBN (Print)	978-3-030-58988-2, 978-3-030-59049-9
DOIs	https://doi.org/10.1007/978-3-030-58989-9_8
Publication status	Published - 12 Sept 2020
MoE publication type	A4 Article in a conference publication
Event	2019 International Conference on Extreme Learning Machine (ELM 2019) - Yangzhou, China Duration: 14 Dec 2019 → 16 Dec 2019

Conference

Conference	2019 International Conference on Extreme Learning Machine (ELM 2019)
Country/Territory	China
City	Yangzhou
Period	14/12/19 → 16/12/19

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

Cite this

@inproceedings{e9e15773ceb94485bee718483a7b2f27,

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. 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.",

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

year = "2020",

month = sep,

day = "12",

doi = "10.1007/978-3-030-58989-9\_8",

language = "English",

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

pages = "69--78",

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

booktitle = "Proceedings of ELM2019",

publisher = "Springer",

address = "Germany",

note = "2019 International Conference on Extreme Learning Machine (ELM 2019) ; Conference date: 14-12-2019 Through 16-12-2019",

}

Espinosa-Leal, L, Akusok, A, Björk, K-M & Lendasse, A 2020, Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning. in J Cao, CM Vong, Y Miche & A Lendasse (eds), Proceedings of ELM2019. Springer, Cham, pp. 69-78, 2019 International Conference on Extreme Learning Machine (ELM 2019), Yangzhou, China, 14/12/19. https://doi.org/10.1007/978-3-030-58989-9_8

Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning. / Espinosa-Leal, Leonardo; Akusok, Anton; Björk, Kaj-Mikael et al.
Proceedings of ELM2019. ed. / Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lendasse. Cham: Springer, 2020. p. 69-78.

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

TY - GEN

T1 - Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

AU - Espinosa-Leal, Leonardo

AU - Akusok, Anton

AU - Björk, Kaj-Mikael

AU - Lendasse, Amaury

PY - 2020/9/12

Y1 - 2020/9/12

N2 - This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM. 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.

AB - This paper presents a novel library for Extreme Learning Machines (ELM) called Scikit-ELM. 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.

UR - https://github.com/akusok/scikit-elm

UR - https://scikit-elm.readthedocs.io

U2 - 10.1007/978-3-030-58989-9_8

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

M3 - Conference article in proceedings

SN - 978-3-030-58988-2

SN - 978-3-030-59049-9

SP - 69

EP - 78

BT - Proceedings of ELM2019

A2 - Cao, Jiuwen

A2 - Vong, Chi Man

A2 - Miche, Yoan

A2 - Lendasse, Amaury

PB - Springer

CY - Cham

T2 - 2019 International Conference on Extreme Learning Machine (ELM 2019)

Y2 - 14 December 2019 through 16 December 2019

ER -

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

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