Genetic algorithms (GAs) have been applied previously to UML-driven, stress test requirements generation with the aim of increasing chances of discovering faults relating to networ...
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
We study nonparametric regression between Riemannian manifolds based on regularized empirical risk minimization. Regularization functionals for mappings between manifolds should re...
In this paper, we introduce Static Execute After (SEA) relationship among program components and present an efficient analysis algorithm. Our case studies show that SEA may appro...
In this work we provide a new methodology for comparing regression functions m1 and m2 from two samples. Since apart from smoothness no other (parametric) assumptions are required...