Abstract
In this paper, we propose a new learning method for multi-class support vector machines based on single class SVM learning method. Unlike the methods 1vs1 and 1vsR, used in the literature and mainly based on binary SVM method, our method learns a classifier for each class from only its samples and then uses these classifiers to obtain a multiclass decision model. To enhance the accuracy of our method, we build from the obtained hyperplanes new hyperplanes, similar to those of the 1vsR method, for use in classification. Our method represents a considerable improvement in the speed of training and classification as well the decision model size while maintaining the same accuracy as other methods.
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References
Dogan, U., Glasmachers, T., Igel, C.: Fast Training of Multi-class Support Vector Machines, Technical Report no. 03/2011, Faculty of science, university of Copenhagen (2011)
Abe, S.: Analysis of multiclass support vector machines. In: Proceedings of International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2003), pp. 385–396 (2003)
Guermeur, Y.: Multi-class Support vector machine, Theory and Applications, HDR thesis, IAEM Lorraine (2007)
Liu, Y., Wang, R., Zeng, Y., He, H.: An Improvement of One-against-all Method for Multiclass Support Vector Machine. In: 4th International Conference: Sciences of Electronic, Technologies of Information and telecommunications, Tunisia, March 25-29 (2007)
Seo, N.: A Comparison of Multi-class Support Vector Machine Methods for Face Recognition, Research report, The University of Maryland (December 2007)
Anthony, G., Gregg, H., Tshilidzi, M.: Image Classification Using SVMs: One-against-One Vs One-against-All. In: Proccedings of the 28th Asian Conference on Remote Sensing (2007)
Foody, M.G., Mathur, A.: A Relative Evaluation of Multiclass Image Classification by Support Vector Machines. IEEE Transactions on Geoscience and Remote Sensing 42, 1335–1343 (2004)
Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010), http://archive.ics.uci.edu/ml
Huang, T., Kecman, V., Kopriva, I.: Kernel Based Algorithms for Mining Huge Data Sets. Springer, Heidelberg (2006)
Wang, L. (ed.): Support Vector Machines: Theory and Applications. Springer, Heidelberg (2005)
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: ICNNSP 1997, New York, pp. 276–285 (1997)
Scholkopf, B., Smola, A.J.: Learning with Kernels Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)
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Abdelhamid, D., Mohamed Chaouki, B., Abdelmalik, TA. (2011). A New Approach to Multi-class SVM Learning Based on OC-SVM for Huge Databases. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25453-6_57
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DOI: https://doi.org/10.1007/978-3-642-25453-6_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25452-9
Online ISBN: 978-3-642-25453-6
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