In this paper, we study an online learning algorithm in Reproducing Kernel Hilbert Spaces (RKHS) and general Hilbert spaces. We present a general form of the stochastic gradient m...
We show that under reasonable conditions, online learning for a nonlinear function near a local minimum is similar to a multivariate Ornstein Uhlenbeck process. This implies that ...
In this paper, we address the problem of learning an
adaptive appearance model for object tracking. In particular,
a class of tracking techniques called “tracking by detectionâ...
This paper builds upon action and design research aimed at enhancing scholarly community and conversation in a graduate school setting. In this paper we focus on knowledge sharing...
Brian Thoms, Nathan Garrett, Jesus Canelon Herrera...
Abstract. Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the ex...
Michael Fussenegger, Peter M. Roth, Horst Bischof,...