Compressive Sensing is an emerging field based on the revelation that a small group of non-adaptive linear projections of a compressible signal contains enough information for rec...
Michael B. Wakin, Jason N. Laska, Marco F. Duarte,...
This paper focuses on the issue of how generalizations of continuous and leftcontinuous t-norms over linearly ordered sets should be from a logical point of view. Taking into acco...
In this paper we generalize the LARS feature selection method to the linear SVM model, derive an efficient algorithm for it, and empirically demonstrate its usefulness as a featur...
Given a 3-valued abstraction of a program (possibly generated using rogram analysis and predicate abstraction) and a temporal logic formula, generalized model checking (GMC) checks...
We consider geometric conditions on a labeled data set which guarantee that boosting algorithms work well when linear classifiers are used as weak learners. We start by providing ...