166 search results - page 12 / 34 » Semi-supervised Learning from Only Positive and Unlabeled Da... |
ALT
2006
Springer
14 years 5 months ago
2006
Springer
A model for learning in the limit is defined where a (so-called iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher ...
JMLR
2008
13 years 8 months ago
2008
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with ...
AUSAI
2008
Springer
13 years 10 months ago
2008
Springer
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
SIGIR
2008
ACM
13 years 8 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...
PAKDD
2005
ACM
14 years 2 months ago
2005
ACM
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the s...