"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...
In this paper a novel high-quality reconstruction scheme is presented. Although our method is mainly proposed to reconstruct volumetric data sampled on an optimal Body-Centered Cu...
This paper shows that scattered range data can be smoothed at low cost by fitting a Radial Basis Function (RBF) to the data and convolving with a smoothing kernel (low pass filt...
Jonathan C. Carr, Richard K. Beatson, Bruce C. McC...
This paper aims to improve the accuracy of query result-size estimations in query optimizers by leveraging the dynamic feedback obtained from observations on the executed query wo...
In the context of the analysis of measured data, one is often faced with the task to differentiate data numerically. Typically, this occurs when measured data are concerned or dat...