ארמין שמילוביץ

The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the influence of the features on the relevance vectors. This requires a single training of
the RVM, thus, it is very efficient. Experiments on a benchmark regression problem provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods.