Abstract. In recent years, particle filters have emerged as a useful tool that enables the application of Bayesian reasoning to problems requiring dynamic state estimation. The ef...
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our a...
We propose a novel approach for improving level set seg-
mentation methods by embedding the potential functions
from a discriminatively trained conditional random field
(CRF) in...
Dana Cobzas (University of Alberta), Mark Schmidt ...
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection...