Statistical Learning Theory: A primer

International Journal of Computer Vision

Volumn 38, Issue 1, 2000, Pages 9-13

  Evgeniou, Theodoros ^a   Pontil, Massimiliano ^a   Poggio, Tomaso ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; RISK MANAGEMENT; STATISTICAL METHODS;

REGULARIZATION NETWORKS; STATISTICAL LEARNING THEORY; STRUCTURAL RISK MINIMIZATION; SUPPORT VECTOR MACHINES (SVM);

COMPUTER VISION;

EID: 0034205755     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008110632619     Document Type: Article

References (17)

1. Alon, N., Ben-David, S., Cesa-Bianchi, N., and Haussler, D. 1993. Scale-sensitive dimensions, uniform convergence, and learnability. Symposium on Foundations of Computer Science.
2. Cortes, C. and Vapnik, V. 1995. Support vector networks. Machine Learning, 20:1-25.
3. Devroye, L., Györfi, L., and Lugosi, G. 1996. A Probabilistic Theory of Pattern Recognition, No. 31 in Applications of Mathematics. Springer: New York.
4. Evgeniou, T., Pontil, M., Papageorgiou, C., and Poggio, T. 2000. Image representations for object detection using kernel classifiers. In Proceedings ACCV. Taiwan, p. To appear.
5. Evgeniou, T., Pontil, M., and Poggio, T. 1999. A unified framework for Regularization Networks and Support Vector Machines. A.I. Memo No. 1654, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
6. Ezzat, T. and Poggio, T. 1996. Facial analysis and synthesis using image-based models. In Face and Gesture Recognition, pp. 116-121.
7. Girosi, F., Jones, M., and Poggio, T. 1995. Regularization theory and neural networks architectures. Neural Computation, 7:219-269.
8. Jaakkola, T. and Haussler, D. 1998. Probabilistic kernel regression models. In Proc. of Neural Information Processing Conference.
9. Kearns, M. and Shapire, R. 1994. Efficient distribution-free learning of probabilistic concepts. Journal of Computer and Systems Sciences, 48(3):464-497.
10. Mohan, A. 1999. Robust object detection in images by components. Master's Thesis, Massachusetts Institute of Technology.
11. Osuna, E., Freund, R., and Girosi, F. 1997. An improved training algorithm for support vector machines. In IEEE Workshop on Neural Networks and Signal Processing, Amelia Island, FL.
12. Papageorgiou, C., Oren, M., and Poggio, T. 1998. A general framework for object detection. In Proceedings of the International Conference on Computer Vision, Bombay, India.
13. Platt, J.C. 1998. Sequential minimal imization: A fast algorithm for training support vector machines. Technical Report MST-TR-98-14, Microsoft Research.
14. Tikhonov, A.N. and Arsenin, V.Y. 1977. Solutions of Ill-posed Problems. Washington, D.C.: W.H. Winston.
15. Vapnik, V.N. 1998. Statistical Learning Theory. Wiley: New York.
16. Vapnik, V.N. and Chervonenkis, A.Y. 1971. On the uniform convergence of relative frequences of events to their probabilities. Th. Prob. and its Applications, 17(2):264-280.
17. Wahba, G. 1990. Splines Models for Observational Data. Vol. 59, Series in Applied Mathematics: Philadelphia.