690 search results - page 27 / 138 » Training of Classifiers Using Virtual Samples Only |
JMLR
2012
11 years 10 months ago
2012
We study the prevalent problem when a test distribution differs from the training distribution. We consider a setting where our training set consists of a small number of sample d...
ICDM
2006
IEEE
14 years 1 months ago
2006
IEEE
Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class exa...
FLAIRS
2004
13 years 9 months ago
2004
Novelty detection in time series is an important problem with application in different domains such as machine failure detection, fraud detection and auditing. An approach to this...
IBPRIA
2005
Springer
14 years 1 months ago
2005
Springer
A natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noi...
AUSAI
2008
Springer
13 years 9 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...