Laplace propagation

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Smola, Alex J ^a   Vishwanathan, S V N ^a   Eskin, Eleazar ^b  

^a CSIRO   (Australia)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; PROBABILITY DISTRIBUTIONS;

APPROXIMATE INFERENCE; BELIEF PROPAGATION; COMMITTEE MACHINES; CONDITIONAL PROBABILITIES; EXPECTATION PROPAGATION; GAUSSIAN PROCESSES; LAPLACE APPROXIMATION; OPTIMIZATION STRATEGY;

LAPLACE TRANSFORMS;

EID: 17644421285     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998.
2. R. Collobert, S. Bengio, and Y. Bengio. A parallel mixture of svms for very large scale problems. In Advances in Neural Information Processing Systems. MIT Press, 2002.
3. S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representations. Journal of Machine Learning Research, 2:243-264, Dec 2001. http://www.jmlr.org.
4. T. Joachims. Making large-scale SVM learning practical. In B. Scḧolkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods-Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
5. M. I. Jordan, Z. Gharamani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In Learning in Graphical Models, volume M. I. Jordan, pages 105-162. Kluwer Academic, 1998.
6. T. Minka. Expectation Propagation for approximative Bayesian inference. PhD thesis, MIT Media Labs, Cambridge, USA, 2001.
7. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan-Kaufman, 1988.
8. J. C. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research, 1998.
9. V. Tresp. A Bayesian committee machine. Neural Computation, 12(11):2719-2741, 2000.