Bayesian regularization and pruning using a Laplace prior
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Bayesian parameter estimation via variational methods
Statistics and Computing
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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In the paper we propose a new type of regularization procedure for training sparse Bayesian methods for classification. Transforming Hessian matrix of log-likelihood function to diagonal form with further regularization of its eigenvectors allows us to optimize evidence explicitly as a product of one-dimensional integrals. The process of automatic regularization coefficients determination then converges in one iteration. We show how to use the proposed approach for Gaussian and Laplace priors. Both algorithms show comparable performance with the state-of-the-art Relevance Vector Machines (RVM) but require less time for training and produce more sparse decision rules (in terms of degrees of freedom).