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CORR
2006
Springer

Generalization error bounds in semi-supervised classification under the cluster assumption

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Generalization error bounds in semi-supervised classification under the cluster assumption
We consider semi-supervised classification when part of the available data is unlabeled. These unlabeled data can be useful for the classification problem when we make an assumption relating the behavior of the regression function to that of the marginal distribution. Seeger (2000) proposed the well-known cluster assumption as a reasonable one. We propose a mathematical formulation of this assumption and a method based on density level sets estimation that takes advantage of it to achieve fast rates of convergence both in the number of unlabeled examples and the number of labeled examples.
Philippe Rigollet
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2006
Where CORR
Authors Philippe Rigollet
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