We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Westudyjobschedulingonprocessorscapableofrunningatvariablevoltage/speed tominimizeenergyconsumption.Eachjob ina probleminstanceisspecifiedbyitsarrivaltime and deadline, together w...
We propose a methodfor segmenting gray-value images. By segmentation, we mean a map from the set of pixels to a small set of levels such that each connected component of the set o...
A generalization of Young's inequality for convolution with sharp constant is conjectured for scenarios where more than two functions are being convolved, and it is proven for...