Abstract
In this work, a novel supervised learning method, the Minimal Learning Machine (MLM), is proposed. Learning a MLM consists in reconstructing the mapping existing between input and output distance matrices and then estimating the response from the geometrical configuration of the output points. Given its general formulation, the Minimal Learning Machine is inherently capable to operate on nonlinear regression problems as well as on multidimensional response spaces. In addition, an intuitive extension of the MLM is proposed to deal with classification problems. On the basis of our experiments, the Minimal Learning Machine is able to achieve accuracies that are comparable to many de facto standard methods for regression and it offers a computationally valid alternative to such approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cox, T., Cox, M.: Multidimensional Scaling. Chapman & Hall, London (1994)
de Silva, V., Tenenbaum, J.B.: Global versus local methods in nonlinear dimensionality reduction. In: Advances in Neural Information Processing Systems, vol. 15, pp. 705–712 (2002)
Huang, G.B., Zhu, Q.Y., Ziew, C.K.: Extreme Learning Machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006)
Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: Optimally Pruned Extreme Learning Machine. IEEE Transactions on Neural Networks 21(1), 158–162 (2010)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199–222 (2004)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2006)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology 2(27), 1–27 (2011)
Lehmann, E.L., Romano, J.P.: Testing statistical hypotheses. Springer, New York (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Souza Junior, A.H., Corona, F., Miche, Y., Lendasse, A., Barreto, G.A., Simula, O. (2013). Minimal Learning Machine: A New Distance-Based Method for Supervised Learning. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-38679-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38678-7
Online ISBN: 978-3-642-38679-4
eBook Packages: Computer ScienceComputer Science (R0)