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.
CITATION STYLE
Matias, A. L. S., Mattos, C. L. C., Kärkkäinen, T., Gomes, J. P. P., & da Rocha Neto, A. R. (2019). OnMLM: An Online Formulation for the Minimal Learning Machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11506 LNCS, pp. 557–568). Springer Verlag. https://doi.org/10.1007/978-3-030-20521-8_46
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