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The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers
Luca Oneto · Davide Anguita · Alessandro Ghio · Sandro Ridella

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection and error estimation of linear (kernel) classifiers, which exploit the availability of unlabeled samples. In particular, two results are obtained: the first one shows that, using the unlabeled samples, the confidence term of the conventional bound can be reduced by a factor of three; the second one shows that the unlabeled samples can be used to obtain much tighter bounds, by building localized versions of the hypothesis class containing the optimal classifier.

Author Information

Luca Oneto (University of Genoa)
Davide Anguita (University of Genoa)
Alessandro Ghio (University of Genova)
Sandro Ridella (University of Genoa, Italy)

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