We present a multi-label multiple kernel learning (MKL) formulation in which the data are embedded into a low-dimensional space directed by the instancelabel correlations encoded ...
Active learning sequentially selects unlabeled instances to label with the goal of reducing the effort needed to learn a good classifier. Most previous studies in active learning...
In this paper we discuss reliable methods in the field of finite precision geometry. We begin with a brief survey of geometric computing and approaches generally used in dealing ...
We develop a family of upper and lower bounds on the worst-case expected KL loss for estimating a discrete distribution on a finite number m of points, given N i.i.d. samples. Our...
We show a simple and efficient way for rendering arbitrary views from so-called free-form light fields, employing a convex free form camera surface and a set of arbitrarily orient...
Hartmut Schirmacher, Christian Vogelgsang, Hans-Pe...