A4 Article in conference proceedings
OnMLM : An Online Formulation for the Minimal Learning Machine (2019)


Matias, Alan L. S.; Mattos, César L. C.; Kärkkäinen, Tommi; Gomes, João P. P.; Rocha Neto, Ajalmar R. da (2019). OnMLM : An Online Formulation for the Minimal Learning Machine. In Rojas, Ignacio; Joya, Gonzalo; Catala, Andreu (Eds.) IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I, Lecture Notes in Computer Science, 11506. Cham: Springer, 557-568. DOI: 10.1007/978-3-030-20521-8_46


JYU authors or editors


Publication details

All authors or editors: Matias, Alan L. S.; Mattos, César L. C.; Kärkkäinen, Tommi; Gomes, João P. P.; Rocha Neto, Ajalmar R. da

Parent publication: IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I

Parent publication editors: Rojas, Ignacio; Joya, Gonzalo; Catala, Andreu

Place and date of conference: Gran Canaria, Spain, 12.-14.6.2019

ISBN: 978-3-030-20520-1

eISBN: 978-3-030-20521-8

Journal or series: Lecture Notes in Computer Science

ISSN: 0302-9743

eISSN: 1611-3349

Publication year: 2019

Number in series: 11506

Pages range: 557-568

Number of pages in the book: 940

Publisher: Springer

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-20521-8_46

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65933


Abstract

Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our experiments, in both classification and regression scenarios, indicate its feasibility for applications that require an online learning framework.


Keywords: machine learning; stochastic processes; big data

Free keywords: online learning; incremental learning; stochastic optimization; Minimal Learning Machine


Contributing organizations


Related projects

STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
Kärkkäinen, Tommi
Academy of Finland
01/01/2018-31/12/2021


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2020-18-08 at 13:48