Abstract
We propose in this work a nested version of the well–known Sequential Minimal Optimization (SMO) algorithm, able to contemplate working sets of larger cardinality for solving Support Vector Machine (SVM) learning problems. Contrary to several other proposals in literature, neither new procedures nor numerical QP optimizations must be implemented, since our proposal exploits the conventional SMO method in its core. Preliminary tests on benchmarking datasets allow to demonstrate the effectiveness of the presented method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bulatov, Y.: (2011), dataset, http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
Fan, R., Chen, P., Lin, C.: Working set selection using second order information for training support vector machines. The Journal of Machine Learning Research 6, 1889–1918 (2005)
Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification (2003)
Joachims, T.: Making large-scale svm learning practical. In: Advances in Kernel Methods (1999)
Keerthi, S., Shevade, S., Bhattacharyya, C., Murthy, K.: Improvements to platt’s smo algorithm for svm classifier design. Neural Computation 13(3), 637–649 (2001)
Larochelle, H., Erhan, D., Courville, A., Bergstra, J., Bengio, Y.: An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the International Conference on Machine Learning, pp. 473–480 (2007)
Lin, Y., Hsieh, J., Wu, H., Jeng, J.: Three-parameter sequential minimal optimization for support vector machines. Neurocomputing 74(17), 3467–3475 (2011)
Munder, S., Gavrila, D.: An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1863–1868 (2006)
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: Proceedings of the Workshop Neural Networks for Signal Processing (1997)
Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. In: Advances in Kernel Methods Support Vector Learning, vol. 208, pp. 1–21 (1998)
Platt, J.: Using analytic qp and sparseness to speed training of support vector machines. In: Advances in Neural Information Processing Systems, pp. 557–563 (1999)
Shawe-Taylor, J., Sun, S.: A review of optimization methodologies in support vector machines. Neurocomputing 74(17), 3609–3618 (2011)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Webb, S., Caverlee, J., Pu, C.: Introducing the webb spam corpus: Using email spam to identify web spam automatically. In: Proceedings of the Conference on Email and Anti-Spam (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ghio, A., Anguita, D., Oneto, L., Ridella, S., Schatten, C. (2012). Nested Sequential Minimal Optimization for Support Vector Machines. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-33266-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33265-4
Online ISBN: 978-3-642-33266-1
eBook Packages: Computer ScienceComputer Science (R0)