Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexiti...
Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Ber...
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
As Computer Science (CS) educators, we are involved in teaching a variety of undergraduate and graduate courses such as database management systems, networking, software developme...
Bhagyavati, Stan Kurkovsky, Christopher C. Whitehe...
In this report, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decisio...