Abstract
In this article, with the aim to avoid the loss of information that occurs in the usual one-versus-one SVM decomposition procedure of the two-phases (decomposition, reconstruction) multi-classification scheme tri-class SVM approach is addressed. As the most relevant result, it will be demonstrated the robustness improvement of the proposed scheme based on tri-class machine versus that based on the bi-class machine.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aiolli, F., Sperduti, A.: Multiclass classification with multi-prototype support vector machine. Journal of Machine Learning Research 6, 817–850 (2005)
Angulo, C.: Learning with Kernel Machines into a Multi-Class Environment (In Spanish). Doctoral thesis, Technical University of Catalonia (April 2001)
Angulo, C., González, L.: 1-v-1 Tri-Class SV Machine. In: Proceedings of the 11th European Symposium on Artificial Neural Networks, ESANN, pp. 355–360 (2003)
Angulo, C., Parra, X., Català, A.: K-SVCR. A support vector machine for multi-class classification. Neurocomputing 55(1-2), 57–77 (2003)
Angulo, C., Ruiz, F., González, L., Ortega, J.A.: Multi-classification by using tri-class SVM. Neural Proceeding Letters 23(1), 89–101 (2006)
Bennett, K., Bredensteiner, E.: Duality and geometry in SVM classifiers. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 57–64. Morgan Kaufmann, San Francisco (2000)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292 (2001)
Crammer, K., Singer, Y.: On the learnability and design of output codes for multiclass problems. Machine Learning 47(2-3), 201–233 (2002)
Crammer, K., Singer, Y.: Pranking with ranking. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14, pp. 641–647. MIT Press, Cambridge (2002)
Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
González, L.: Discriminative analysis using support vector kernel machines. The similitary kernel function (In Spanish). Master’s thesis, Dept. of Applied Economy I. University of Seville (2002)
González, L., Angulo, C., Velasco, F., Vilchez, M.: Máquina ℓ-SVCR con salidas probabilísticas. Inteligencia Artificial 17, 72–82 (2002)
González, L., Angulo, C., Velasco, F., Catala, A.: Unification of all bi-class SVM approaches. Pattern Recognition 38(10), 1772–1774 (2005)
González, L., Angulo, C., Velasco, F., Catala, A.: Dual unification of bi-class support vector machine formulations. Pattern Recognition 39(7), 1325–1332 (2006)
Hein, M., Bousquet, O.: Maximal Margin Classification for Metric Spaces. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 72–86. Springer, Heidelberg (2003)
Kressel, U.: Pairwise classification and support vector machine. In: Advances in Kernel Methods: Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Platt, J., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. In: Neural Information Processing Systems, vol. 12 (2000)
Schölkopf, B., Smola, A.: Learning with Kernels. The MIT Press, Cambridge (2002)
Shashua, A., Levin, A.: Ranking with large margin principle: Two approaches. In: Thrun, S., Becker, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15, pp. 937–944. MIT Press, Cambridge (2003)
Shashua, A., Levin, A.: Taxonomy of large margin principle algorithms for ordinal regression problems. In: Neural Information Processing Systems, vol. 16 (2002)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Zhong, P., Fukushima, M.: A new multi-class support vector algorithm. Optimization Methods and Software 21(3), 359–372 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Angulo, C., González, L., Català, A., Velasco, F. (2007). Multi-classification with Tri-class Support Vector Machines. A Review. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-73007-1_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
eBook Packages: Computer ScienceComputer Science (R0)