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 ...
A comparative case study used content analysis to observe the emergence of voluntary online learning communities in two blended courses. The study developed an interaction-based a...
One problem of data-driven answer extraction in open-domain factoid question answering is that the class distribution of labeled training data is fairly imbalanced. This imbalance...
Michael Wiegand, Jochen L. Leidner, Dietrich Klako...
We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L2 regularization: We introduce the -adapted-dimension, which...
- Peer-to-peer (P2P) offers good solutions for many applications such as large data sharing and collaboration. Thus, it appears as a powerful paradigm to develop scalable distribut...
Stainam N. Brandao, Ricardo T. Silva, Jano M. Souz...