We propose an adaptive algorithm for control of combustion instability suitable for reduction of acoustic pressure oscillations in gas turbine engines, and main burners and augmen...
Andrzej Banaszuk, Kartik B. Ariyur, Miroslav Krsti...
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
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 ...
As the number of Mobile IP users grows, so will the signalling overhead associated with Internet mobility management in the core IP network. This presents a significant challenge t...
Xiaowei Zhang, Javier Gomez Castellanos, Andrew T....