In this paper we propose a genetic programming approach to learning stochastic models with unsymmetrical noise distributions. Most learning algorithms try to learn from noisy data...
We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a...
David H. Stern, Horst Samulowitz, Ralf Herbrich, T...
Effective use of the memory hierarchy is critical for achieving high performance on embedded systems. We focus on the class of streaming applications, which is increasingly preval...
Janis Sermulins, William Thies, Rodric M. Rabbah, ...
Clinical studies are often conducted as multi-centered studies involving participants at different locations. Thus it becomes obvious that using mobile platforms and remote data e...
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...