OnMLM : An Online Formulation for the Minimal Learning Machine
Matias, A. L. S., Mattos, C. L. C., Kärkkäinen, T., Gomes, J. P.P., & Rocha Neto, A. R. D. (2019). OnMLM : An Online Formulation for the Minimal Learning Machine. In I. Rojas, G. Joya, & A. Catala (Eds.), IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I (pp. 557-568). Springer. Lecture Notes in Computer Science, 11506. https://doi.org/10.1007/978-3-030-20521-8_46
Julkaistu sarjassa
Lecture Notes in Computer ScienceTekijät
Päivämäärä
2019Tekijänoikeudet
© Springer International Publishing AG 2019
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.
Julkaisija
SpringerEmojulkaisun ISBN
978-3-030-20520-1Konferenssi
International Work-Conference on Artificial Neural NetworksKuuluu julkaisuun
IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part IISSN Hae Julkaisufoorumista
0302-9743Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/32145299
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Akatemiaohjelma, SA; Profilointi, SALisätietoja rahoituksesta
The work was supported by the Academy of Finland from grants 311877 and 315550.Lisenssi
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