Abstract. Data mining, which aims at extracting interesting information from large collections of data, has been widely used as an effective decision making tool. Mining the datas...
We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations usi...
Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consi...
Sauro Menchetti, Andrea Passerini, Paolo Frasconi,...
The research literature investigating the construction of tutorial dialogue and learning companion environments present parallel experiences in attempting to emulate what has been ...
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...