A variational approach to robust regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2130, Issue , 2001, Pages 95-102

  Faul, Anita C ^a   Tipping, Michael E ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BAYESIAN FRAMEWORKS; OUTLYING OBSERVATION; REGRESSION ESTIMATION; ROBUST REGRESSIONS; VARIATIONAL APPROACHES; VARIATIONAL APPROXIMATION;

NEURAL NETWORKS;

EID: 84958972434     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44668-0_14     Document Type: Conference Paper

References (5)

1. C. M. Bishop and M. E. Tipping. Variational relevance vector machines. In C. Boutilier and M. Goldszmidt, editors, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pages 46–53. Morgan Kaufmann, 2000.
2. D. J. C. MacKay. Bayesian interpolation. Neural Computation, 4(3):415–447, 1992.
3. W. Penny and S. J. Roberts. Variational Bayes for non-Gaussian autoregressive models. In Neural Networks for Signal Processing X, pages 135–144. IEEE, 2000.
4. M. E. Tipping. The Relevance Vector Machine. In S. A. Solla, T. K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems 12, pages 652–658. MIT Press, 2000.
5. S. Waterhouse, D. J. C. MacKay, and T. Robinson. Bayesian methods for mixtures of experts. In M. C. Mozer, D. S. Touretzky, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8. MIT Press, 1996.