On the performance of kernel classes

Journal of Machine Learning Research

Volumn 4, Issue 5, 2004, Pages 759-771

  Mendelson, Shahar ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; EIGENVALUES AND EIGENFUNCTIONS; FUNCTIONS; MATHEMATICAL OPERATORS; PROBABILITY; THEOREM PROVING;

FEATURE MAP; HILBERT SPACE; INTEGRAL OPERATORS; KERNEL CLASSES;

LEARNING SYSTEMS;

EID: 3142722249     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/1532443041424337     Document Type: Conference Paper

References (13)

1. P.L. Bartlett, O. Bousquet, and S. Mendelson. Localized Rademacher complexities. 2003. Preprint.
2. P.L. Bartlett and S. Mendelson. Empirical risk minimization. 2003. Preprint.
3. O. Bousquet. Concentration inequalities and empirical processes theory applied to the analysis of learning algorithms. PhD thesis, Ecole Polytechnique, Paris, 2002.
4. P. Cucker and S. Smale. On the mathematical foundations of learning. Bulletin of the AMS, 39(1):1-49, 2002.
5. P. Cucker and S. Smale. Best choices for regularization parameters in learning theory: on the bias-variance problem. Foundations of Computational Mathematics, 2(4):413-428, 2003.
6. G. Lugosi and M. Wegkamp. Complexity regularization via localized random penalties. Annals of Statistics, 2003. to appear.
7. P. Massart. About the constants in Talagrand's concentration inequality for empirical processes. Annals of Probability, 28(2):863-884, 2000.
8. S. Mendelson and G. Schechtman. The shattering dimension of sets of linear functionals. Annals of Probability, 2003. to appear.
9. S. Mendelson. Improving the sample complexity using global data. IEEE Transactions on Information Theory, 48(7):1977-1991, 2002.
10. V.D. Milman and G. Schechtman. Asymptotic theory of finite dimensional normed spaces. Lecture Notes in Mathematics 1200. Springer, 1986.
11. G. Pisier. The Volume of Convex Bodies and Banach Space Geometry. Cambridge University Press, Cambridge, 1989.
12. A. W. van der Vaart and J. A. Wellner. Weak Convergence and Empirical Processes. Springer, 1996.
13. T. Zhang. Effective dimension and generalization of kernel learning. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 454-461. MIT Press, Cambridge, MA, 2003.