Abstract
Support Vector Machines are finding application in pattern recognition, regression estimation, and operator inversion. To extend the using range, people have always been trying their best in finding online algorithms. But the Support Vector Machines are sensitive only to the extreme values and not to the distribution of the whole data. Ordinary algorithm can not predict which value will be sensitive and has to deal with all the data once. This paper introduces an algorithm that selects promising vectors from given vectors. Whenever a new vector is added to the training data set, unnecessary vectors are found and deleted. So we could easily get an online algorithm. We give the reason we delete unnecessary vectors, provide the computing method to find them. At last, we provide an example to illustrate the validity of algorithm.
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
Boser, B.E., Guyon, I.M., Vapnik, V.: A Training Algorithm for Optimal Margin Classifers. In: Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, pp. 144–152. ACM, New York (1992)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Scholkopf, B., Mika, S., Burges, C.J.C., Knirsch, P.: Input Space Versus Feature Space in Kernel-Based Methods. IEEE Trans. on Neural Networks, 1000–1016 (1999)
Aronszajn, N.: Theory of Reproducing Kernels. Trans. Amer. Math. Soc., 337–404 (1950)
LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Dener, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of Learning Algorithms for Handwritten Digital Recognition. In: Fogelman, F., Gallinari, P. (eds.) International Conference on Artificial Neural Networks, pp. 53–60 (1995)
Vapnik, V.: Statistical Learning Theory, pp. 401–408. Wiley, New York (1998)
Platt, J.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machine. Technical Report MSR-TR-98-14. Microsoft Research (1998)
Osuna, E., Freund, R., Girosi, F.: An Improved Algorithm for Support Vector Machines. In: Proc. Of NNSP 1997 (1997)
Mangasarian, O.L., Musicant, D.R.: Successive Overrelaxation for Support Vector Machine. IEEE Transactions on Neural Networks 10, 1032–1037 (1999)
Mangasarian, O.L., Musicant, D.R.: Active Support Vector Machine Classification. In: Advances in Neural Information Processing Systems, NIPS 2000 (2000)
Mangasarian, O.L., Musicant, D.R.: Lagrangian Support Vector Machines. Journal of Machine Learning Research 1, 161–177 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gan, L., Sun, Z., Sun, Y. (2005). Online Support Vector Machines with Vectors Sieving Method. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_134
Download citation
DOI: https://doi.org/10.1007/11427391_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
eBook Packages: Computer ScienceComputer Science (R0)