The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data...
One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributi...
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 ...
We describe here a methodology to combine two different techniques for Semantic Relation Extraction from texts. On the one hand, generic lexicosyntactic patterns are applied to the...
In this paper we describe a software tool that allows investigators to make comparisons across different online forums and media by analyzing word counts in userspecified categori...
Adam D. I. Kramer, Susan R. Fussell, Leslie D. Set...