The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
We present a data-driven, unsupervised method for unusual
scene detection from static webcams. Such time-lapse
data is usually captured with very low or varying framerate.
This ...
Michael D. Breitenstein, Helmut Grabner, Luc Van G...
A critical success factor in Insurance business is its ability to use information sources and contained knowledge in the most effective way. Its profitability is obtained through t...
Andrea Tettamanzi, Luca Sammartino, Mikhail Simono...
We introduce relational temporal difference learning as an effective approach to solving multi-agent Markov decision problems with large state spaces. Our algorithm uses temporal ...
The paper describes our first experiments on Reinforcement Learning to steer a real robot car. The applied method, Neural Fitted Q Iteration (NFQ) is purely data-driven based on ...
Martin Riedmiller, Michael Montemerlo, Hendrik Dah...