78 search results - page 3 / 16 » Learning and evaluating classifiers under sample selection b... |
ICDM
2003
IEEE
13 years 10 months 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...
GECCO
2009
Springer
13 years 9 months ago
2009
Springer
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
TNN
2010
13 years 17 hour ago
2010
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
KDD
2009
ACM
14 years 5 months ago
2009
ACM
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
ISDA
2010
IEEE
13 years 3 months ago
2010
IEEE
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are signific...