78 search results - page 1 / 16 » Learning and evaluating classifiers under sample selection b... |
ICML
2004
IEEE
14 years 5 months ago
2004
IEEE
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
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...
ISPDC
2010
IEEE
13 years 3 months ago
2010
IEEE
—Providing independent uniform samples from a system population poses considerable problems in highly dynamic settings, like P2P systems, where the number of participants and the...
JMLR
2010
12 years 11 months ago
2010
Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of...
CVPR
2001
IEEE
14 years 6 months ago
2001
IEEE
All positive examples are alike; each negative example is negative in its own way. During interactive multimedia information retrieval, the number of training samples fed-back by ...