Multi-instance learning deals with problems that treat bags of instances as training examples. In single-instance learning problems, dimensionality reduction is an essential step ...
A Bayesian belief network is a model of a joint distribution over a finite set of variables, with a DAG structure representing immediate dependencies among the variables. For each...
We describe the results of a research on the effect of weight-decay (WD) in input selection methods based on the analysis of a trained multilayer feedforward network. It was propos...
The goal of the Globe project is to design and build a middleware platform that facilitates the development of large-scale distributed applications, such as those found on the Int...
Arno Bakker, E. Amade, Gerco Ballintijn, Ihor Kuz,...
This paper proposes a fine granularity scalable (FGS) coding using cycle-based leaky prediction, in which the multiple leaky factors are used to yield enhancement layer prediction ...
Xiangyang Ji, Yanyan Zheng, Debin Zhao, Feng Wu, W...