In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have ...
Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Ma...
This paper considers using online binary classification for target detection where the goal is to identify signals of interest within a sequence of received signals generated by ...
We study the problem of mining frequent value sets from a large sensor network. We discuss how sensor stream data could be represented that facilitates efficient online mining and ...
Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single de...
When integrating data from multiple sources, a key task that online communities often face is to match the schemas of the data sources. Today, such matching often incurs a huge wor...