This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for...
This paper presents a new non-iterative, closed-form approximation to the maximum entropy (M.E.) image restoration method. A fast frequency domain implementation of this closed fo...
Covariance matrix tapering (CMT) is a popular approach to improve the robustness of adaptive beamformers against moving or wideband interferers. In this paper, we develop a comput...
This paper deals with the problem of optimally matching two ordered sets of 3-D points by means of a rigid displacement. Contrary to the standard approach, where a sumof-squared-er...
We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and prediction...