421 search results - page 11 / 85 » Reducing Label Complexity by Learning From Bags |
FLAIRS
2004
14 years 11 months ago
2004
In our prior work, we introduced a generalization of the multiple-instance learning (MIL) model in which a bag's label is not based on a single instance's proximity to a...
ASUNAM
2010
IEEE
14 years 11 months ago
2010
IEEE
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
ML
2000
ACM
14 years 9 months ago
2000
ACM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
69
Voted
ECIR
2007
Springer
14 years 11 months ago
2007
Springer
Automated text categorisation systems learn a generalised hypothesis from large numbers of labelled examples. However, in many domains labelled data is scarce and expensive to obta...
CEAS
2007
Springer
15 years 1 months ago
2007
Springer
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...