Support Vector Machines: A Large Scale QP Problem
In this paper a new type of learning machines, named Support Vector Machines (SVMs), are discussed. In essence, given a training set — i.e., a number of previously classified patterns —, SVMs perform effective pattern recognition on a set of previously unseen patterns. We first review the theory of SCMs and some of their mathematical properties in detail. Then, we describe a few methods for the implementation of SVMs, which in the general case of large training sets requires the solution of large scale Quadratic Programming (QP) problem. Finally, we report the experimental results of the application of SCMs for the solution of a computer vision problem, appearance based 3-D object recognition from single image.
KeywordsSupport Vector Machine Support Vector Feature Space Quadratic Programming Margin Vector
Unable to display preview. Download preview PDF.
- 2.B. Boser, I. Guyoni, and V. Vapnik, training algorithm for optimal margin classifier, Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. Pittsburg, PA, 1992.Google Scholar
- 3.C. Cortes and V.N. Vapnik, Support Vector Network. Machine learning, Vol. 20, 1995, pp. 1–25.Google Scholar
- 4.R. Courant and D. Hilbert. Methods of Mathematical Physics. John Wiley, New York, 1959.Google Scholar
- 5.L. Kaufman. Solving the Quadratic Programming problem arising in support vector classification. Technical report Bell Labs 1997.Google Scholar
- 7.E. Osuna, R. Freund, and F. Girosi. Support Vector Machines: training and applications, A.I. Memo 1602, MIT A.I. Lab., 1997.Google Scholar
- 8.E. Osuna, R. Freund and F. Girosi, An improved training algorithm for Support Vector Machines, IEEE Workshop on Neural Networks and Signal Processing, Amelia Island, FL, 1997.Google Scholar
- 9.C. Papageorgiou, M. Oren and T. Poggio. A General Framework for Object Detection. Proceedings of the International Conference on Computer Vision, Bombay, India, 1998.Google Scholar
- 12.M. Schmit, Identifying speakers with support vectors machines. Proceedings of Interface, Sydney, 1996.Google Scholar
- 13.Vanderbei, R.J. 1997. LOQO User’s Manual — Version 3.04. Statistical and Operations Research Tech. Rep. SOR-97-07, 1997.Google Scholar