The nature of statistical learning theory
The nature of statistical learning theory
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Online Model Selection Based on the Variational Bayes
Neural Computation
Neural Computation
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Sequential Bayesian kernel modelling with non-Gaussian noise
Neural Networks
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The Relevance Vector Machine (RVM) gives a probabilistic model for a sparse kernel representation. It achieves comparable performance to the Support Vector Machine (SVM) while using substantially fewer kernel bases. However, the computational complexityof the RVM in the training phase prohibits its application to large datasets. In order to overcome this difficulty, we propose an incremental Bayesian method for the RVM. The preliminaryexp eriments showed the efficiencyof our method for large datasets.