221 search results - page 9 / 45 » Learning from Labeled and Unlabeled Data Using Random Walks |
NIPS
2004
13 years 9 months ago
2004
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...
CVPR
2007
IEEE
14 years 10 months ago
2007
IEEE
Current methods for learning visual categories work well when a large amount of labeled data is available, but can run into severe difficulties when the number of labeled examples...
CVPR
2006
IEEE
14 years 10 months ago
2006
IEEE
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
ACL
2009
13 years 5 months ago
2009
For many NLP tasks, including named entity tagging, semi-supervised learning has been proposed as a reasonable alternative to methods that require annotating large amounts of trai...
SIGIR
2008
ACM
13 years 7 months ago
2008
ACM
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...