73 search results - page 2 / 15 » Learning to Classify Documents with Only a Small Positive Tr... |
CVPR
2007
IEEE
13 years 11 months ago
2007
IEEE
Aiming at the problem when both positive and negative training set are enormous, this paper proposes a novel Matrix-Structural Learning (MSL) method, as an extension to Viola and ...
SDM
2008
SIAM
13 years 6 months ago
2008
SIAM
Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
AAAI
1998
13 years 6 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...
ADCS
2004
13 years 6 months ago
2004
Co-training is a semi-supervised technique that allows classifiers to learn with fewer labelled documents by taking advantage of the more abundant unclassified documents. However, ...
AUSAI
2008
Springer
13 years 6 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...