We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels fo...
Andreas Argyriou, Raphael Hauser, Charles A. Micch...
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Abstract. Geoscience analysis is currently limited by cumbersome access and manipulation of large datasets from remote sources. Due to their data-heavy and compute-light nature, th...
Daniel L. Wang, Charles S. Zender, Stephen F. Jenk...
Science is becoming data-intensive, requiring new software architectures that can exploit resources at all scales: local GPUs for interactive visualization, server-side multi-core ...
Keith Grochow, Bill Howe, Mark Stoermer, Roger S. ...
Abstract—A variational approach is proposed for the unsupervised assessment of attribute variability of high-dimensional data given a differentiable similarity measure. The key q...