"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vecto...
In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a pri...
This paper presents a method to quantitatively evaluate
information contributions of individual bottom-up and topdown
computing processes in object recognition. Our objective
is...