The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the...
We give an algorithm for the bandit version of a very general online optimization problem considered by Kalai and Vempala [1], for the case of an adaptive adversary. In this proble...
Abstract—We point out a problem inherent in the optimization scheme of many popular feature selection methods. It follows from the implicit assumption that higher feature selecti...
In this paper, we study the problem of nonnegative graph
embedding, originally investigated in [14] for reaping the
benefits from both nonnegative data factorization and the
spe...
Changhu Wang (University of Science and Technology...
High assurance in embedded system software is difficult to attain. Verification relies on testing. The unreliable and costly testing process is made much worse because the softwar...