BER-Based Call Admission In Wavelength-Routed Optical Networks

Consider a wavelength-routed optical network in which nodes, i.e., multiwave length cross-connect switches (XCSs), are connected by fiber to form an arbitrary physical topology. A new call is admitted into the network if an all-optical lightpath can be established between the call’s source and destination nodes. Wavelength converters are assumed absent in this work.

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.