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 this work we consider the task of relaxing the i.i.d assumption in online pattern recognition (or classification), aiming to make existing learning algorithms applicable to a ...
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
In this work we consider the task of relaxing the i.i.d. assumption in pattern recognition (or classification), aiming to make existing learning algorithms applicable to a wider r...
Abstract - Certain structure detection problems can be solved by sampling a parameter space for the different structures at a finite number of points and checking each point to see...