Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
The Bayesian backfitting relevance vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Predictive automatic relevance determination by expectation propagation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Building Sparse Large Margin Classifiers
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
| Hi-index | 0.00 |
We present an approximate Bayesian method for regression and classification with models linear in the parameters. Similar to the Relevance Vector Machine (RVM), each parameter is associated with an expansion vector. Unlike the RVM, the number of expansion vectors is specified beforehand. We assume an overall Gaussian prior on the parameters and find, with a gradient based process, the expansion vectors that (locally) maximize the evidence. This approach has lower computational demands than the RVM, and has the advantage that the vectors do not necessarily belong to the training set. Therefore, in principle, better vectors can be found. Furthermore, other hyperparameters can be learned in the same smooth joint optimization. Experimental results show that the freedom of the expansion vectors to be located away from the training data causes overfitting problems. These problems are alleviated by including a hyperprior that penalizes expansion vectors located far away from the input data.