We develop improved risk bounds for function estimation with models such as single hidden layer neural nets, using a penalized least squares criterion to select the size of the mod...
Abstract. In this paper, we propose a new framework for the parametric verification of time Petri nets with stopwatches controlled by inhibitor arcs. We first introduce an extensio...
Louis-Marie Traonouez, Didier Lime, Olivier H. Rou...
The paper proposes a new measure for the cohesion of classes in Object-Oriented software systems. It is based on the analysis of latent topics embedded in comments and identifiers...
Yixun Liu, Denys Poshyvanyk, Rudolf Ferenc, Tibor ...
Discovering rare categories and classifying new instances of them is
an important data mining issue in many fields, but fully supervised
learning of a rare class classifier is pr...
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...