Abstract
Extreme learning machine (ELM) has been studied extensively in recent years. It is a very simple machine learning algorithm which can achieve a good generalization performance with extremely fast speed. Thus, it has practical significance for Big Data analysis. Normally, it is implemented under the empirical risk minimization scheme and it may tend to generate a large-scale and over-fitting model. In this paper, an ELM model based on L 1-norm and L 2-norm regularizations is proposed to deal with regression and multiple class classification problems in a unified framework, and it can reduce the complexity of the network and prevent over-fitting. We test the proposed algorithm on eight benchmark data sets. Simulation results have shown that the proposed algorithm outperforms the original ELM and other advanced ELM algorithm in terms of prediction accuracy and stability.
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: A New Learning Scheme of Feedforward Neural Networks. In: IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE Press, New York (2004)
Horata, P., Chiewchanwattana, S., Sunat, K.: Robust Extreme Learning Machine. Neurocomputing 102, 31–44 (2013)
Rong, H.J., Ong, Y.S., Tan, A.H., Zhu, Z.: A Fast Pruned-Extreme Learning Machine for Classification Problem. Neurocomputing 2, 359–366 (2008)
MartÃnez-MartÃnez, J.M., Escandell-Montero, P., Soria-Olivas, E., Martn-Guerrero, J.D., Magdalena-Benedito, R., Gómez-Sanchis, J.: Regularized Extreme Learning Machine for Regression Problems. Neurocomputing 74, 3716–3721 (2011)
Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., Lendasse, A.: OP-ELM: Optimally Pruned Extreme Learning Machine. IEEE Trans. Neural Networks 21, 158–162 (2010)
Zou, H., Hastie, T.: Regularization and Variable Selection via the Elastic Net. J. R. Statist. Soc. B 67, 301–320 (2005)
Burnham, K.P., Anderson, D.R.: Multimodel Inference Understanding AIC and BIC in Model Selection. Sociological Methods & Res 33, 261–304 (2004)
Cao, J., Lin, Z., Huang, G.B., Liu, N.: Voting Based Extreme Learning Machine. Inf. Sci. 1, 66–77 (2012)
Tibshirani, R.: Regression Shrinkage and Selection via the Lasso. J. R. Statist. Soc. B 58, 267–288 (1996)
Jacob, L., Obozinski, G., Vert, J.P.: Group Lasso with Overlap and Graph Lasso. In: 26th Annual International Conference on Machine Learning, pp. 433–440. ACM Press, New York (2009)
De Mol, C., De Vito, E., Rosasco, L.: Elastic-Net Regularization in Learning Theory. J. Complexity 25, 201–230 (2009)
Bache, K., Lichman, M.: UCI Machine Learning Repository (2013), http://archive.ics.uci.edu/ml/
Grigorievskiy, A., Miche, Y., Ventelä, A.M., Séverin, E., Lendasse, A.: Long-Term Time Series Prediction Using OP-ELM. Neural Netw. 51, 50–56 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Luo, X., Chang, X., Ban, X. (2015). Extreme Learning Machine for Regression and Classification Using L 1-Norm and L 2-Norm. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-14063-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-14063-6_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14062-9
Online ISBN: 978-3-319-14063-6
eBook Packages: EngineeringEngineering (R0)