354 search results - page 4 / 71 » Learning classifiers from only positive and unlabeled data |
NIPS
1998
1998
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
13 years 8 months ago
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
DAGM
2004
Springer
14 years 26 days ago
2004
Springer
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
ML
2000
ACM
13 years 7 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. ...
ICDM
2008
IEEE
14 years 1 months ago
2008
IEEE
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
ICPR
2006
IEEE
14 years 8 months ago
2006
IEEE
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...