A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Kärkkäinen, Tommi

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Kärkkäinen, Tommi

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

Konferenssi:

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

ISBN: 978-2-87587-047-6

Julkaisuvuosi: 2018

Artikkelin sivunumerot: 237-242

Kustantaja: ESANN

Julkaisumaa: Belgia

Julkaisun kieli: englanti

Pysyvä verkko-osoite: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-72.pdf

Julkaisun avoin saatavuus: Muulla tavalla avoin

Julkaisukanavan avoin saatavuus:

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/62746

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

Tiivistelmä

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.

YSO-asiasanat: koneoppiminen

Vapaat asiasanat: Extreme Learning Machine; Minimal Learning Machine

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