Several difficulties arise when testing network security algorithms. First, using network data captured at a router does not guarantee that any instances of the security event of...
Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on...
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
In this contribution, we explore the possibilities of learning in large-scale, multimodal processing systems operating under real-world conditions. Using an instance of a large-sca...
The foremost nonlinear dimensionality reduction algorithms provide an embedding only for the given training data, with no straightforward extension for test points. This shortcomin...