This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
Given observed data and a collection of parameterized candidate models, a 1- confidence region in parameter space provides useful insight as to those models which are a good fit t...
Brent Bryan, H. Brendan McMahan, Chad M. Schafer, ...
In large-scale clusters and computational grids, component failures become norms instead of exceptions. Failure occurrence as well as its impact on system performance and operatio...
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
We present a powerful meta-clustering technique called Iterative Double Clustering (IDC). The IDC method is a natural extension of the recent Double Clustering (DC) method of Slon...