Finite sample error bound for parzen windows

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2005, Pages 925-930

  Zhang, Peng ^a   Peng, Jing ^a   Riedel, Norbert ^a  

^a Tulane University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION PROBLEMS; DENSITY ESTIMATION; FINITE SAMPLE ERROR BOUND; PARZEN WINDOWS;

CLASSIFICATION (OF INFORMATION); ERROR CORRECTION; LEAST SQUARES APPROXIMATIONS; PROBLEM SOLVING; STATISTICAL METHODS;

LEARNING SYSTEMS;

EID: 29344440308     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Cristianini, N., and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge, UK: Cambridge University Press.
2. Cucker, F., and Smale, S. 2001, On the mathematical foundations of learning. Bulletin of the American Mathematical Society 39(1):1-49.
3. Cucker, F., and Smale, S. 2002. Best choices for regularization parameters in learning theory: On the bias-variance problem. Foundations Comput. Math. (4):413-428.
4. Duda, R.; Hart, P.; and Stork, D. 2000. Patten Classification, 2nd edition. John-Wiley.
5. Evgeniou, T.; Pontil, M.; and Poggio, T. 2000. Regularization networks and support vector machines. Advances in Computational Mathematics 13(1):1-50.
6. Fukumizu, K.; Bach, F. R.; and Jordan, M. I. 2004. Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces. Journal of Machine Learning Research 5:73-99.
7. Fukunaga, K. 1990. Introduction to statistical pattern recognition. Academic Press.
8. Hoerl, A., and Kennard, R. 1970. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(3):55-67.
9. J. Hertz, A. K., and Palmer, R. 1991. Introduction to the Theory of Neural Computation. Addison Wesley.
10. Mitchell, T. 1997. Machine learning. McGraw Hill.
11. Parzen, E. 1962. On the estimation of a probability density function and the mode. Ann. Math. Stats. 33:1049-1051.
12. Poggio, T., and Smale, S. 2003. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society 50(5):537-544.
13. Tikhonov, A. N., and Arsenin, V. Y. 1977. Solutions of Illposed problems. Washington D.C.: John Wiley and Sons.
14. Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.