High-performance ensembles of online sequential extreme learning machine for regression and time series forecasting

High-performance ensembles of online sequential extreme learning machine for regression and time series forecasting

Luís Fernando L. Grim, André Leon S. Gradvohl

ARTIGO

Inglês

Agradecimentos: The authors would like to acknowledge the HPI Future SOC Lab for providing the computing resources. The first author would also like to acknowledge the Federal Institute of São Paulo at Piracicaba for supporting his research

Este artigo foi apresentado no evento International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), 2018

Abstract: Ensembles of Online Sequential Extreme Learning Machine algorithm are suitable for forecasting Data Streams with Concept Drifts. Nevertheless, data streams forecasting require high-performance implementations due to the high incoming samples rate. In this work, we proposed to tune-up three...

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High-performance ensembles of online sequential extreme learning machine for regression and time series forecasting

Luís Fernando L. Grim, André Leon S. Gradvohl

										

High-performance ensembles of online sequential extreme learning machine for regression and time series forecasting

Luís Fernando L. Grim, André Leon S. Gradvohl

    Fontes

    Proceedings of the 30th International Symposium on Computer Architecture and High Performance Computing

    Piscataway, NJ : Institute of Electrical and Electronics Engineers, 2018.

    p. 394-401