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NIPS
1998
1998
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
13 years 9 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...
AAAI
1998
13 years 9 months ago
1998
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
IJCNLP
2004
Springer
14 years 1 months ago
2004
Springer
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this pap...
ICDM
2003
IEEE
14 years 28 days 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...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...