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References

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C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20, 1995.

2
R. Courant and D. Hilbert. Methods of Mathematical Physiacs, volume 1. Interscience, New-York, 1953.

3
N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and other Kernel-based learning methods. Cambridge University Press, 2000.

4
B. Heisele, T. Poggio, and M. Pontil. Face detection in still gray images. Technical Report AI Memo 1687, MIT AI Lab, 2000.

5
B. Moghaddam and M.-H. Yang. Gender classification with support vector machines. In Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pages 306-311, 2000.

6
M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio. Pedestrian detection using wavelet templates. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 193-199, 1997.

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E. Osuna, R. Freund, and F. Girosi. Training support vector machines: an application to face detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 130-136, 1997.

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C. Papageorgiou and T. Poggio. A trainable system for object detection. International Journal of Computer Vision, 38(1):15-33, 2000.

9
M. Pontil and A. Verri. Support vector machines for 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6):637-646, 1998.

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D. Roobaert and M. V. Hulle. View-based 3d object recognition with support vector machines. In IEEE International Workshop on Neural Networks for Signal Processing, 1999.

11
B. Schölkopf. Support Vector Learning. PhD thesis, Informatik der Technischen Universitat Berlin, 1997.

12
A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors. Advances in Large Margin Classifiers. MIT Press, 2000.

13
V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.


Bob Fisher
Wednesday March 14 14:58:48 GMT 2001