Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
In this paper we show how frequent sequence mining (FSM) can be applied to data produced by monitoring distributed enterprise applications. In particular we show how we applied FSM...
Abstract. E cient data mining algorithms are crucial fore ective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a ...
Abstract. This work proposes a family of language-independent semantic kernel functions defined for individuals in an ontology. This allows exploiting wellfounded kernel methods fo...
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin ...