In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
In this paper, we propose a novel method to reduce the magnitude of 4D CT artifacts by stitching two images with a data-driven regularization constrain, which helps preserve the l...
Dongfeng Han, John Bayouth, Qi Song, sudershan Bha...
A structural similarity kernel is presented in this paper for SVM learning, especially for learning with imbalanced datasets. Kernels in SVM are usually pairwise, comparing the sim...
: The main contribution of this paper is a novel approach for fast searching in huge structural databases like the PDB. The data structure is based on an adaption of the generalize...
This paper describes a new algorithm for recovering the
3D shape and motion of deformable and articulated objects
purely from uncalibrated 2D image measurements using an
iterati...