Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
Abstract--This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simple...
In this research, we investigate and address the challenges of asymmetry in High-End Computing (HEC) systems comprising heterogeneous architectures with varying I/O and computation...
In multicluster systems, and more generally, in grids, jobs may require co-allocation, i.e., the simultaneous allocation of resources such as processors and input files in multip...
We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumpti...