Abstract

In diverse machine learning problems ranging from automated currency validation (ACV) to protein fold prediction, we encounter the situation where multiple object descriptors are available for a possibly multinomial classification task. Specifically, ACV considers the challenging and unresolved problem of counterfeit note detection while depositing currency in an ATM that is equipped with a plurality of sensors. In an analogous manner, when predicting the structural fold of a protein multiple feature sets are available, ranging from global characteristics like the amino-acid composition and predicted secondary structure, to attributes derived from local sequence alignment such as the Smith-Waterman scores. These problems raise the need for a classification method that is able to assess the contribution of these potentially heterogeneous object descriptors while utilizing such information to improve predictive performance. In this talk I will present a hierarchical Bayesian multi-class multi-kernel pattern recognition machine that informatively combines the available feature groups and, as is demonstrated, is able to provide the state-of-the-art in performance accuracy on the problems considered. The full Markov chain Monte Carlo solution of the model is offered via a Metropolis-Hastings within Gibbs sampling procedure and also an efficient variational Bayes approximation is proposed.