We propose a highly efficient framework for penalized likelihood kernel methods applied to multiclass models with a large, structured set of classes. As opposed to many previous a...
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applicati...
Given an arbitrary network of interconnected nodes, we develop and analyze a distributed strategy that enables a subset of the nodes to calculate any given function of the node val...
Wireless networks are currently experiencing more overload situations than their wireline counterparts because of explosive mobile traffic growth, unpredictable traffic behavior, s...
Finding interaction patterns is a challenging problem, but this kind of information about processes or social networks might be useful for an organization’s management to unders...