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From Automated Currency Validation to Protein Fold Recognition: Probabilistic Multi-class Multi-kernel Learning
If you have a question about this talk, please contact David MacKay.
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.
This talk is part of the Inference Group series.
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