We apply robust Bayesian decision theory to improve both generative and discriminative learners under bias in class proportions in labeled training data, when the true class propo...
: We developed a steerable interface system that can direct graphical displays to any desirable locations, can capture interactions at any desirable locations, and can track user l...
Piyawadee Noi Sukaviriya, Mark Podlaseck, Rick Kje...
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensionality spaces, often revealing the true intrinsic dimensio...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...
esult abstracts viewed and those clicked on, and whether gender, search task, or search engine influence these behaviors. In addition, we discuss a key challenge that arose in all ...
We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consis...
Yair Goldberg, Alon Zakai, Dan Kushnir, Yaacov Rit...