395 search results - page 9 / 79 » Learning to rank with partially-labeled data |
138
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KDD
2009
ACM
16 years 2 months ago
2009
ACM
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
142
Voted
CVPR
2009
IEEE
16 years 9 months ago
2009
IEEE
Latent Variable Models (LVM), like the Shared-GPLVM
and the Spectral Latent Variable Model, help mitigate over-
fitting when learning discriminative methods from small or
modera...
115
Voted
CVPR
2008
IEEE
16 years 3 months ago
2008
IEEE
Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling a ...
111
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FGR
2008
IEEE
15 years 8 months ago
2008
IEEE
Linear Discriminant Analysis (LDA) has been a popular method for feature extracting and face recognition. As a supervised method, it requires manually labeled samples for training...
125
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WSDM
2010
ACM
15 years 11 months ago
2010
ACM
In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work ha...