63 search results - page 1 / 13 » Efficiently learning the accuracy of labeling sources for se... |
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...
PAMI
2006
13 years 4 months ago
2006
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...
JMLR
2006
13 years 4 months ago
2006
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
NIPS
2004
13 years 6 months ago
2004
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...