Theory, Implementation, and Applications of Support Vector Machines
Support Vector Machines (SVMs) have been recently introduced as a new method for function estimation. In this survey we first review the main theoretical properties of SVMs, then present an implementation of SVMs able to work with training sets of very large size. Finally, we discuss two computer vision applications in which SVMs for both pattern recognition and regression estimation have been successfully employed.
KeywordsSupport Vector Machine Support Vector Regression Estimation Structural Risk Minimi Computer Vision Application
Unable to display preview. Download preview PDF.
- Cortes, C. and Vapnik, V.N. “Support Vector Network”, Machine learning 20: 1–25 (1995).Google Scholar
- Evgenious, T., Pontil, M., and Poggio, T. “A Unified Framework for Regularization Networks and Support Vector Machines”, A.I. Memo 1654, MIT A.I. Lab. (1999).Google Scholar
- Odone, F., Trucco, E., and Verri, A. “Visual Learning of Weight from Shape Using Support Vector Machines”, In Proc. British Machine Vis. Conf., Southampton, 469–477 (1998).Google Scholar
- Osuna, E., Freund, R., and Girosi, F. “Support Vector Machines: training and applications”, A.I. Memo 1602, MIT A.I. Lab. (1997).Google Scholar
- Pallavicini, M., Patrignani, C., Pontil, M., e Verri, A. “The Nearest-Neighbor Technique for Particle Identification”, Nuclear Instruments and Methods in Physics Research. A 405. 133–138 (1998).Google Scholar
- Papageorgiou, C., Oren, M., and Poggio, T. “A General Framework for Object Detection”, In Proc. Int. Conf. Computer Vision, Bombay, 555–562 (1998).Google Scholar
- Pittore, M., Basso, C. e Verri, A. “Representing and Recognizing Visual Dynamic Events with Support Vector Machines”, In Proc. Int. Conf. Image Analysis and Proc., Venice (1999).Google Scholar
- Vapnik, V.N. The Nature of Statistical Learning Theory, Springer, 1995.Google Scholar
- Vapnik, V.N. Statistical Learning Theory, Wiley, 1998.Google Scholar