Search Results for author:
Found 1 papers, 0 papers with code
The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
