Variational Gaussian process classifiers

@article{Gibbs2000VariationalGP,
  title={Variational Gaussian process classifiers},
  author={Mark N. Gibbs and D. Mackay},
  journal={IEEE transactions on neural networks},
  year={2000},
  volume={11 6},
  pages={
          1458-64
        }
}
  • Mark N. Gibbs, D. Mackay
  • Published 2000
  • Mathematics, Computer Science, Medicine
  • IEEE transactions on neural networks
Gaussian processes are a promising nonlinear regression tool, but it is not straightforward to solve classification problems with them. In this paper the variational methods of Jaakkola and Jordan are applied to Gaussian processes to produce an efficient Bayesian binary classifier. 
SVM regression through variational methods and its sequential implementation
Dependent Gaussian Processes
Bayesian Kernel Methods
Efficient Approaches to Gaussian Process Classification
General Bounds on Bayes Errors for Regression with Gaussian Processes
Mean Field Methods for Classification with Gaussian Processes
GAUSSIAN REGRESSION BASED ON MODELS WITH TWO STOCHASTIC PROCESSES
Gaussian Process Classification Using Posterior Linearization
Clustering Based on Gaussian Processes
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References

SHOWING 1-10 OF 29 REFERENCES
Regression with Gaussian processes
Gaussian Processes for Regression
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo
Bayesian parameter estimation via variational methods
Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
Flexible Non-linear Approaches to Classification
The Evidence Framework Applied to Classification Networks
  • D. Mackay
  • Mathematics, Computer Science
  • Neural Computation
  • 1992
Computation with Infinite Neural Networks
Computing upper and lower bounds on likelihoods in intractable networks
Pattern Recognition and Neural Networks
...
1
2
3
...