We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
We examine the number T of queries that a quantum network requires to compute several Boolean functions on f0;1gN in the black-box model. We show that, in the blackbox model, the ...
Robert Beals, Harry Buhrman, Richard Cleve, Michel...
We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. Thi...
In this paper, we study a class of optimal path problems with the following phenomenon: The space complexity of the algorithms for reporting the lengths of single-source optimal pa...
Danny Z. Chen, Ovidiu Daescu, Xiaobo Hu, Jinhui Xu
The block processing inherent in the use of traditional vector quantization (VQ) schemes typically gives rise to perceptually distracting blocking artifacts. We demonstrate that s...
Henrique S. Malvar, Gary J. Sullivan, Gregory W. W...