—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
In imaging genomics, there have been rapid advances in genome-wide, image-wide searches for genes that influence brain structure. Most efforts focus on univariate tests that treat...
Derrek P. Hibar, Jason L. Stein, Omid Kohannim, Ne...
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Lea...
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect inform...