OnMLM : An Online Formulation for the Minimal Learning Machine
Matias, Alan L. S.; Mattos, César L. C.; Kärkkäinen, Tommi; Gomes, João P. P.; Rocha Neto, Ajalmar R. da (2019). OnMLM : An Online Formulation for the Minimal Learning Machine. In Rojas, Ignacio; Joya, Gonzalo; Catala, Andreu (Eds.) IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I, Lecture Notes in Computer Science, 11506. Cham: Springer, 557-568. DOI: 10.1007/978-3-030-20521-8_46
Published inLecture Notes in Computer Science
© 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.
Parent publication ISBN978-3-030-20520-1
ConferenceInternational Work-Conference on Artificial Neural Networks
Is part of publicationIWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part I
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Others, AoF; Academy Programme, AoF
Additional information about fundingThe work was supported by the Academy of Finland from grants 311877 and 315550.
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