Abstract
This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM methodology is considerably faster than the MLP and the LS-SVM, while maintaining the accuracy in the same level. Finally, a toolbox performing the OP-ELM is introduced and instructions are presented.
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
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Miller, W.T., Glanz, F.H., Kraft, L.G.: Cmac: An associative neural network alternative to backpropagation. Proceedings of the IEEE 70, 1561–1567 (1990)
Rao, C.R., Mitra, S.K.: Generalized Inverse of Matrices and Its Applications. John Wiley & Sons, Chichester (1972)
Similä, T., Tikka, J.: Multiresponse sparse regression with application to multidimensional scaling. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 97–102. Springer, Heidelberg (2005)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32, 407–499 (2004)
Myers, R.: Classical and Modern Regression with Applications, 2nd edn. Duxbury, Pacific Grove (1990)
Bontempi, G., Birattari, M., Bersini, H.: Recursive lazy learning for modeling and control. In: European Conference on Machine Learning, pp. 292–303 (1998)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Englewood Cliffs (1998)
Suykens, J., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vanderwalle, J.: Least-Squares Support-Vector Machines. World Scientific, Singapore (2002)
Lendasse, A., Sorjamaa, A., Miche, Y.: OP-ELM Toolbox, http://www.cis.hut.fi/projects/tsp/index.php?page=research&subpage=downloads
Whitney, A.W.: A direct method of nonparametric measurement selection. IEEE Transactions on Computers C-20, 1100–1103 (1971)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miche, Y., Sorjamaa, A., Lendasse, A. (2008). OP-ELM: Theory, Experiments and a Toolbox. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-87536-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
Online ISBN: 978-3-540-87536-9
eBook Packages: Computer ScienceComputer Science (R0)