The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
Given observed data and a collection of parameterized candidate models, a 1- confidence region in parameter space provides useful insight as to those models which are a good fit t...
Brent Bryan, H. Brendan McMahan, Chad M. Schafer, ...
Controlled experiments are a key approach to evaluate and evolve our understanding of software engineering technologies. However, defining and running a controlled experiment is a...
Today, systems should react based on explicit demands from the learner or even proactively react based on changes in the working environment. The success of this type of systems de...
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...