Abstract
We discuss incremental training of support vector machines in which we approximate the regions, where support vector candidates exist, by truncated hypercones. We generate the truncated surface with the center being the center of unbounded support vectors and with the radius being the maximum distance from the center to support vectors. We determine the hypercone surface so that it includes a datum, which is far away from the separating hyperplane. Then to cope with non-separable cases, we shift the truncated hypercone along the rotating axis in parallel in the opposite direction of the separating hyperplane. We delete the data that are in the truncated hypercone and keep the remaining data as support vector candidates. In computer experiments, we show that we can delete many data without deteriorating the generalization ability.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Mitra, P., Murthy, C.A., Pal, S.K.: Data Condensation in Large Databases by Incremental Learning with Support Vector Machines. In: Proc. ICPR 2000, pp. 2708–2711 (2000)
Domeniconi, C., Gunopulos, D.: Incremental Support Vector Machine Construction. In: Proc. ICDM 2001, pp. 589–592 (2001)
Cauwenberghs, G., Poggio, T.: Incremental and Decremental Support Vector Machine Learning. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 409–415. MIT Press, Cambridge (2000)
Katagiri, S., Abe, S.: Selecting Support Vector Candidates for Incremental Training. In: Proc. SMC 2005, pp. 1258–1263 (2005)
Katagiri, S., Abe, S.: Incremental Training of Support Vector Machines Using Hyperspheres. Pattern Recognition Letters (accepted)
Tax, D.M.J., Duin, R.P.W.: Outliers and Data Descriptions. In: Proc. Seventh Annual Conference of the Advanced School for Computing and Imaging, pp. 234–241 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katagiri, S., Abe, S. (2006). Incremental Training of Support Vector Machines Using Truncated Hypercones. In: Schwenker, F., Marinai, S. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2006. Lecture Notes in Computer Science(), vol 4087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829898_14
Download citation
DOI: https://doi.org/10.1007/11829898_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37951-5
Online ISBN: 978-3-540-37952-2
eBook Packages: Computer ScienceComputer Science (R0)