A4 Article in conference proceedings
Extreme Minimal Learning Machine (2018)

Kärkkäinen, T. (2018). Extreme Minimal Learning Machine. In ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 237-242). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf

JYU authors or editors

Kärkkäinen, Tommi

Publication details

All authors or editors: Kärkkäinen, Tommi

Parent publication: ESANN 2018 : Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference:

European symposium on artificial neural networks, computational intelligence and machine learning

ISBN: 978-2-87587-047-6

Publication year: 2018

Pages range: 237-242

Publisher: ESANN

Publication country: Belgium

Publication language: English

Persistent website address: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf

Publication open access: Other way freely accessible online

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/62746

Additional information: ESANN 2018 : 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 25-26-27, 2018.

Abstract

Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM) are nonlinear and scalable machine learning techniques with randomly generated basis. Both techniques share a step where a matrix of weights for the linear combination of the basis is recovered. In MLM, the kernel in this step corresponds to distance calculations between the training data and a set of reference points, whereas in ELM transformation with a sigmoidal activation function is most commonly used. MLM then needs additional interpolation step to estimate the actual distance-regression based output. A natural combination of these two techniques is proposed here, i.e., to use a distance-based kernel characteristic in MLM in ELM. The experimental results show promising potential of the proposed technique.Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM) are nonlinear and scalable machine learning techniques with randomly generated basis. Both techniques share a step where a matrix of weights for the linear combination of the basis is recovered. In MLM, the kernel in this step corresponds to distance calculations between the training data and a set of reference points, whereas in ELM transformation with a sigmoidal activation function is most commonly used. MLM then needs additional interpolation step to estimate the actual distance-regression based output. A natural combination of these two techniques is proposed here, i.e., to use a distance-based kernel characteristic in MLM in ELM. The experimental results show promising potential of the proposed technique.

Keywords: machine learning

Free keywords: Extreme Learning Machine; Minimal Learning Machine

Fields of science: