Abstract
The well-known and very simple MinOver algorithm is reformulated for incremental support vector classification with and without kernels. A modified proof for its \(\mathcal{O}(t^{1/2})\) convergence is presented, with t as the number of training steps. Based on this modified proof it is shown that even a convergence of at least \(\mathcal{O}(t^{1})\) is given. This new convergence bound for MinOver is confirmed by computer experiments on artificial data sets. The computational effort per training step scales as \(\mathcal{O}(N)\) with the number N of training patterns.
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References
Cortes, C., Vapnik, V.: Support-vector-networks. Machine Learning 20(3), 273–297 (1995)
Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. In: Computational Learing Theory, pp. 209–217 (1998)
Friess, T.T., Cristianini, N., Campbell, C.: The kernel adatron algorithm: a fast and simple learning procedure for support vector machine. In: Proc. 15th International Conference on Machine Learning (1998)
Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: A fast iterative nearest point algorithm for support vector machine classifier design. IEEE-NN 11(1), 124–136 (2000)
Krauth, W., Mezard, M.: Learning algorithms with optimal stability in neural networks. J.Phys.A 20, 745–752 (1987)
LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of learning algorithms for handwritten digit recognition. In: Int.Conf.on Artificial Neural Networks, pp. 53–60 (1995)
Li, Y., Long, P.M.: The relaxed online maximum margin algorithm. Machine Learning 46(1-3), 361–387 (2002)
Navone, H.D., Downs, T.: Variations on a kernel-adatron theme. VII Internacional Congress on Information Engineering, Buenos Aires (2001)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines:an application to face detection. In: CVPR 1997, pp. 130–136 (1997)
Platt, J.C.: Advances in Kernel Methods - Support Vector Learning. In: chapter Fast Training of Support Vector Machines using Sequential Minimal Optimization, pp. 185–208. MIT Press, Cambridge (1999)
Schölkopf, B.: Support vector learning (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
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© 2004 Springer-Verlag Berlin Heidelberg
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Martinetz, T. (2004). MinOver Revisited for Incremental Support-Vector-Classification. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_23
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DOI: https://doi.org/10.1007/978-3-540-28649-3_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
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