  The Relevance Vector Machine (RVM) is a recently developed machine learning
framework capable of building simple models from large sets of candidate
features. Here, we describe a protocol for using the RVM to explore very large
numbers of candidate features, and a family of models which apply the power of
the RVM to classifying and detecting interesting points and regions in
biological sequence data. The models described here have been used successfully
for predicting transcription start sites and other features in genome
sequences.
