Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Sparse Bayesian modeling with adaptive kernel learning
IEEE Transactions on Neural Networks
Embedding relevance vector machine in fuzzy inference system for energy consumption forecasting
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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A Bayesian learning algorithm is presented that is based on a sparse Bayesian linear model (the Relevance Vector Machine (RVM)) and learns the parameters of the kernels during model training. The novel characteristic of the method is that it enables the introduction of parameters called `scaling factors' that measure the significance of each feature. Using the Bayesian framework, a sparsity promoting prior is then imposed on the scaling factors in order to eliminate irrelevant features. Feature selection is local, because different values are estimated for the scaling factors of each kernel, therefore different features are considered significant at different regions of the input space. We present experimental results on artificial data to demonstrate the advantages of the proposed model and then we evaluate our method on several commonly used regression and classification datasets.