COLT
2005
Springer
13 years 10 months ago
2005
Springer
We present a new generalization bound where the use of unlabeled examples results in a better ratio between training-set size and the resulting classifier’s quality and thus red...
AWIC
2007
Springer
13 years 11 months ago
2007
Springer
A major difficulty of supervised approaches for text classification is that they require a great number of training instances in order to construct an accurate classifier. This pap...
CICLING
2009
Springer
13 years 11 months ago
2009
Springer
Abstract. As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks,...
KDD
2008
ACM
14 years 5 months ago
2008
ACM
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
WWW
2004
ACM
14 years 5 months ago
2004
ACM
In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are...
ICML
2005
IEEE
14 years 5 months ago
2005
IEEE
Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning....
ICCV
2003
IEEE
14 years 6 months ago
2003
IEEE
This paper discusses building complex classifiers from a single labeled example and vast number of unlabeled observation sets, each derived from observation of a single process or...
ICCV
2003
IEEE
14 years 6 months ago
2003
IEEE
One significant challenge in the construction of visual detection systems is the acquisition of sufficient labeled data. This paper describes a new technique for training visual d...