Abstract
In this paper an online learning algorithm based on incremental chunk for LS-SVM (Least Square Support Vector Machines) classifiers is proposed. The training of the LS-SVM can be placed in a way of incremental chunk, which avoids computing large-scale matrix inverse but maintaining the precision when training and testing data. This online algorithm is especially useful for the large data set and practical applications where the data come in sequentially. Our experiments with four classification problems in UCI show that compared with LS-SVM, the computational cost of our algorithm is reduced obviously and the accuracy is retained.
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
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Osuna, E., Freund, R., Girosi, F.: An Improved Training Algorithm for Support Vector Machines. In: Neural Networks for Signal Processing-Proceedings of the IEEE, pp. 276–285 (1997)
Joachims, T.: Making Large-Scale Support Vector Machine Practical. In: Advances in Kernel Methods-Support Vector Learning, pp. 169–184. The MIT Press, Cambridge (1999)
Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods-Support Vector Learning, pp. 185–208. The MIT Press, Cambridge (1999)
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)
Lin, C.J.: On the Convergence of the Decomposition Method for Support Vector Machines. IEEE Transactions on Neural Networks 12(6), 1288–1298 (2001)
Lin, C.J.: Asymptotic Convergence of an SMO Algorithm without any Assumptions. IEEE Transactions on Neural Networks 13(1), 248–250 (2002)
Mangasarian, O.L., Musicant, D.R.: Lagrangian Support Vector Machines. Journal of Machine Learning Research 1, 161–177 (2001)
Yang, X.W., Shu, L., Hao, Z.F.: An extended Lagrangian Support Vector Machine for classification. Progress in Natural Science 14(6), 519–523 (2004)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Process Letter 9, 293–300 (1999)
Chua, K.S.: Efficient computations for large least square support vector machine classifiers. Pattern Recognition Letters 24, 75–80 (2003)
Fine, S., Scheinberg, K.: Efficient SVM Training Using Low-Rank Kernel Representations. Journal of Machine Learning Research 2(2), 243–264 (2002)
Liu, J.H., Chen, J.P., Jiang, S., Cheng, J.S.: Online LS-SVM for function estimation and classification. Journal of University of Science and Technology Beijing 10(5), 73–77 (2003)
Golub, G.H., Van, L.C.F.: Matrix Computations, 3rd edn. The John Hopkins University Press, Baltimore (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hao, Z., Yu, S., Yang, X., Zhao, F., Hu, R., Liang, Y. (2004). Online LS-SVM Learning for Classification Problems Based on Incremental Chunk. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_92
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive