571 search results - page 2 / 115 » Learning with Constrained and Unlabelled Data |
COLING
2008
13 years 6 months ago
2008
In this paper, we focus on the adaptation problem that has a large labeled data in the source domain and a large but unlabeled data in the target domain. Our aim is to learn relia...
MCS
2009
Springer
14 years 2 days ago
2009
Springer
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
COLT
2008
Springer
13 years 7 months ago
2008
Springer
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
ICDM
2003
IEEE
13 years 10 months ago
2003
IEEE
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
NIPS
2008
13 years 6 months ago
2008
Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...