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

^*Motsvarande författare för detta arbete

Finansiell ekonomi, Helsingfors

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

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.

Originalspråk	Engelska
Titel på värdpublikation	Proceedings of ELM2019
Redaktörer	Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
Utgivningsort	Cham
Förlag	Springer
Utgivningsdatum	2021
Sidor	69-78
ISBN (tryckt)	978-3-030-58988-2
ISBN (elektroniskt)	978-3-030-58989-9
DOI	https://doi.org/10.1007/978-3-030-58989-9_8
Status	Publicerad - 2021
MoE-publikationstyp	A4 Artikel i en konferenspublikation

Publikationsserier

Namn	Proceedings in Adaptation, Learning and Optimization
Volym	14
ISSN (tryckt)	2363-6084
ISSN (elektroniskt)	2363-6092

Nyckelord

113 Data- och informationsvetenskap

Åtkomst av dokument

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

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Fulltext

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https://www.mendeley.com/catalogue/97c2a929-4099-3669-8b4e-c9d7d1d4d8ea/

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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. ",

keywords = "113 Computer and information sciences",

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

year = "2021",

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

language = "English",

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

series = "Proceedings in Adaptation, Learning and Optimization",

publisher = "Springer",

pages = "69--78",

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

booktitle = "Proceedings of ELM2019",

address = "International",

}

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. red. / Jiuwen Cao; Chi Man Vong; Yoan Miche; Amaury Lendasse. Cham: Springer, 2021. s. 69-78 (Proceedings in Adaptation, Learning and Optimization; Vol. 14).

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

TY - GEN

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

AU - Akusok, Anton

AU - Leal, Leonardo Espinosa

AU - Björk, Kaj-Mikael

AU - Lendasse, Amaury

PY - 2021

Y1 - 2021

N2 - 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.

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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UR - https://www.mendeley.com/catalogue/97c2a929-4099-3669-8b4e-c9d7d1d4d8ea/

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

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

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SN - 978-3-030-58988-2

T3 - Proceedings in Adaptation, Learning and Optimization

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BT - Proceedings of ELM2019

A2 - Cao, Jiuwen

A2 - Vong, Chi Man

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A2 - Lendasse, Amaury

PB - Springer

CY - Cham

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

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