A Ranking Distance Based Diversity Measure for Multiple Classifier Systems

(To the best of our knowledge, such a hierarchical approach has been considered only once [14], but for a distance devoted to ordinal histogram comparison.) The purpose of the

A way to assess both the accuracy (i.e., the ex- pected fraction of alternatives correctly classified) and the confidence of the classification model (i.e., the probability that

To allow metric learning on high-dimensional image features of large scale datasets that are too large to fit in memory, we use in addition product quantization (Gray and

Premature small jobs have a wildly over-estimated processing time, causing them to wait longer for execution, which results in large slowdown values.. This is problematic as

In this re- search we explore the potential of data synthesis with GANs to address this issue, more specifically we compare data synthesis with the Wasserstein Genera- tive

Although different algorithms have been employed and tested to define the set of classification models composing the Classification module, a subset of them were selected which

The main feature of this classification scheme is that it combines types of diversity from different domains, takes into account new technologies that can be used in safety

In combination with the parallel architecture, however, a significant improvement of the results regarding the basic model is achieved, as well, but it gets clear that in all cases