Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the ...
Our goal is to provide learning mechanisms to game agents so they are capable of adapting to new behaviors based on the actions of other agents. We introduce a new on-line reinfor...
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion c...