Adaptive sparseness using jeffreys prior

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Figueiredo, Mário A T ^a  

^a INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATA; ERROR RATE; JEFFREYS PRIOR; STATE-OF-THE-ART PERFORMANCE; SUPERVISED LEARNING PROBLEMS;

BENCHMARKING;

EID: 4544258714     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. V. Cherkassky and F. Mulier, Learning from Data: Concepts, Theory, and Methods. New York: Wiley, 1998.
2. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, 2000.
3. B. Ripley, Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
4. V. Vapnik, Statistical Learning Theory. New York: John Wiley, 1998.
5. A. Hoerl and R. Kennard, "Ridge regression: Biased estimation for nonorthogonal problems, " Technometrics, vol. 12, pp. 55-67, 1970.
6. C. Bishop, Neural Networks for Pattern Recognition. Oxford University Press, 1995.
7. R. Neal, Bayesian Learning for Neural Networks. New York: Springer Verlag, 1996.
8. C. Williams, "Prediction with Gaussian processes: from linear regression to linear prediction and beyond, " in Learning and Inference in Graphical Models, Kluwer, 1998.
9. C. Williams and D. Barber, "Bayesian classification with Gaussian priors, " IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1342-1351, 1998.
10. G. Kimeldorf and G. Wahba, "A correspondence between Bayesian estimation of stochastic processes and smoothing by splines, " Annals of Mathematical Statistics, vol. 41, pp. 495-502, 1990.
11. T. Poggio and F. Girosi, "Networks for approximation and learning, " Proceedings of the IEEE, vol. 78, pp. 1481-1497, 1990.
12. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit, " SIAM Journal of Scientific Computation, vol. 20, no. 1, pp. 33-61, 1998.
13. F. Girosi, "An equivalence between sparse approximation and support vector machines, " Neural Computation, vol. 10, pp. 1445-1480, 1998.
14. R. Tibshirani, "Regression shrinkage and selection via the lasso, " Journal of the Royal Statistical Society (B), vol. 58, 1996.
15. P. Williams, "Bayesian regularization and pruning using a Laplace prior, " Neural Computation, vol. 7, pp. 117-143, 1995.
16. K. Lange and J. Sinsheimer, "Normal/independent distributions and their applications in robust regression, " Journal of Computational and Graphical Statistics, vol. 2, pp. 175-198, 1993.
17. M. Figueiredo and R. Nowak, "Wavelet-based image estimation: An empirical Bayes approach using Jeffreys' noninformative prior, " IEEE Transactions on Image Processing, vol. 10, pp. 1322-1331, 2001.
18. J. Berger, Statistical Decision Theory and Bayesian Analysis. Springer-Verlag, 1980.
19. D. MacKay, "Bayesian non-linear modelling for the 1993 energy prediction competition, " in Maximum Entropy and Bayesian Methods, G. Heidbreder, ed., pp. 221-234, Kluwer, 1996.
20. C. Bishop and M. Tipping, "Variational relevance vector machines, " in Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, pp. 46-53, Morgan Kaufmann, 2000.
21. M. Tipping, "The relevance vector machine, " in Advances in Neural Information Processing Systems - NIPS 12 (S. Solla, T. Leen, and K.-R. M̈uller, eds.), pp. 652-658, MIT Press, 2000.
22. D. L. Donoho and I. M. Johnstone, "Ideal adaptation via wavelet shrinkage, " Biometrika, vol. 81, pp. 425-455, 1994.
23. M. Osborne, B. Presnell, and B. Turlach, "A new approach to variable selection in least squares problems, " IMA Journal of Numerical Analysis, vol. 20, pp. 389-404, 2000.
24. P. McCullagh and J. Nelder, Generalized Linear Models. London: Chapman and Hall, 1989.
25. J. Albert and S. Chib, "Bayesian analysis of binary and polychotomous response data, " Journal of the American Statistical Association, vol. 88, pp. 669-679, 1993.
26. M. Seeger, "Bayesian model selection for support vector machines, Gaussian processes and other kernel classifiers, " in Advances in Neural Information Processing - NIPS 12 (S. Solla, T. Leen, and K.-R. Muller, eds.), pp. 603-609, MIT Press, 2000.
27. C.Williams and M. Seeger, "Using the Nystrom method to speedup kernel machines, " in NIPS 13, MIT Press, 2001.