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
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Lecture Notes in Computer ScienceAuthors
Date
2019Copyright
© 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.
Publisher
SpringerParent publication ISBN
978-3-030-20520-1Conference
International Work-Conference on Artificial Neural NetworksIs part of publication
IWANN 2019 : Advances in Computational Intelligence : 15th International Work-Conference on Artificial Neural Networks, Proceedings, Part IISSN Search the Publication Forum
0302-9743Keywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/32145299
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Academy of FinlandFunding program(s)
Others, AoF; Academy Programme, AoF
Additional information about funding
The work was supported by the Academy of Finland from grants 311877 and 315550.License
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