We consider the problem of learning the parameters of a Bayesian network from data, while taking into account prior knowledge about the signs of influences between variables. Such...
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems....
Voice over IP (VoIP) speech quality estimation is crucial to providing optimal Quality of Service (QoS). This paper seeks to provide improved speech quality estimation models with...
Adil Raja, R. Muhammad Atif Azad, Colin Flanagan, ...
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional histogram data. Adopting the Bayesian framework, we propose to perform anneale...
We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algor...