We introduce the notion of restricted Bayes optimal classifiers. These classifiers attempt to combine the flexibility of the generative approach to classification with the high ac...
We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the compos...
Feature ranking is a fundamental machine learning task with various applications, including feature selection and decision tree learning. We describe and analyze a new feature ran...
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...