A4 Article in conference proceedings
Model selection for Extreme Minimal Learning Machine using sampling (2019)


Kärkkäinen, T. (2019). Model selection for Extreme Minimal Learning Machine using sampling. In ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 391-396). ESANN. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-18.pdf


JYU authors or editors


Publication details

All authors or editors: Kärkkäinen, Tommi

Parent publication: ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference:

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

Place and date of conference: Bruges, Belgium, 24.-26.4.2019

ISBN: 978-2-87587-065-0

eISBN: 978-2-87587-066-7

Publication year: 2019

Pages range: 391-396

Number of pages in the book: 696

Publisher: ESANN

Publication country: Belgium

Publication language: English

Persistent website address: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-18.pdf

Publication open access: Other way freely accessible online

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/66803


Abstract

A combination of Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM)—to use a distance-based basis from MLM in the ridge regression like learning framework of ELM—was proposed in [8]. In the further experiments with the technique [9], it was concluded that in multilabel classification one can obtain a good validation
error level without overlearning simply by using the whole training data for constructing the basis. Here, we consider possibilities to reduce the complexity of the resulting machine learning model, referred as the Extreme Minimal Leaning Machine (EMLM), by using a bidirectional sampling strategy: To sample both the feature space and the space of observations in order to identify a simpler EMLM without sacrificing its generalization performance.


Keywords: machine learning


Contributing organizations


Related projects


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2024-22-04 at 23:26