Abstract
This paper describes a simple modification to the sequential minimal optimisation (SMO) training algorithm for support vector machine (SVM) classifiers, reducing training time at the expense of a small increase in memory used proportional to the number of training patterns. Results obtained on real-world pattern recognition tasks indicate that the proposed modification can more than halve the average training time.
This work was supported by the European Commission grant number IST-99-11764, as part of its Framework V IST programme.
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References
B. Boser, I. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,’ in Proceedings of the fifth annual workshop on computational learning theory, (Pittsburgh), pp. 144–152, ACM, 1992.
C. Cortes and V. Vapnik, “Support vector networks,” Machine Learning, vol. 20, pp. 1–25, 1995.
V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
V. N. Vapnik, Statistical Learning Theory. Wiley Series on Adaptive and Learning Systems for Signal Processing, Communications and Control, New York: Wiley, 1998.
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge, U.K.: Cambridge University Press, 2000.
J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods — Support Vector Learning (B. Schölkopf, C. J. C. Burges, and A. J. Smola, eds.), ch. 12, pp. 185–208, Cambridge, Massachusetts: MIT Press, 1999.
E. Osuna, R. Freund, and F. Girosi, “An improved training algorithm for support vector machines,” in Neural Networks for Signal Processing VII — Proceedings of the 1997 IEEE Workshop (J. Principe, L. Gile, N. Morgan, and E. Wilson, eds.), (New York), pp. 276–285, IEEE, 1997.
K. Arnold, J. Gosling, and D. Holmes, The Java Programming Language. Addison-Wesley, third ed., 2000.
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© 2001 Springer-Verlag Wien
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Cawley, G.C. (2001). Efficient Sequential Minimal Optimisation of Support Vector Classifiers. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_107
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_107
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
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