Measure based regularization

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Bousquet, Olivier ^a   Chapelle, Olivier ^a   Hein, Matthias ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

SUPERVISED LEARNING;

GRAPH-BASED; MARGINAL DISTRIBUTION; SEMI-SUPERVISED LEARNING; THEORETICAL METHODS;

LEARNING ALGORITHMS;

EID: 84898942071     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373{1396, 2003.
2. M. Belkin and P. Niyogi. Semi-supervised learning on manifolds. Machine Learning journal, 2003. to appear.
3. F. Girosi, M. Jones, and T. Poggio. Priors, stabilizers and basis functions: From regularization to radial, tensor and additive splines. Technical Report Artificial Intelligence Memo 1430, Massachusetts Institute of Technology, 1993.
4. W. Hoefiding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58:13{30, 1963.
5. G. Kimeldorf and G. Wahba. Some results on tchebychean spline functions. Journal of Mathematics Analysis and Applications, 33:82{95, 1971.
6. Doudou La Loudouana and Mambobo Bonouliqui Tarare. Data set selection. In Advances in Neural Information Processing Systems, volume 15, 2002.
7. A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems, volume 14, 2001.
8. B. Scholkopf and A. Smola. Learning with kernels. MIT Press, Cambridge, MA, 2002.
9. A. Smola and B. Scholkopf. On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22:211{231, 1998.
10. M. Szummer and T. Jaakkola. Information regularization with partially labeled data. In Advances in Neural Information Processing Systems, volume 15. MIT Press, 2002.
11. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319{2323, 2000.
12. P. Vincent and Y. Bengio. Density-sensitive metrics and kernels. Presented at the Snowbird Learning Workshop, 2003.
13. U. von Luxburg and O. Bousquet. Distance-based classification with lipschitz functions. In Proceedings of the 16th Annual Conference on Computational Learning Theory, 2003.