In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New re...
Alessio Dore, Andrea F. Cattoni, Carlo S. Regazzon...
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by ta...
— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do t...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...