Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised protein classification using cluster kernels
Bioinformatics
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A PAC-Style model for learning from labeled and unlabeled data
COLT'05 Proceedings of the 18th annual conference on Learning Theory
A boosting approach to multiview classification with cooperation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
| Hi-index | 0.00 |
The paper considers the problem of semi-supervised multiview classification, where each view corresponds to a Reproducing Kernel Hilbert Space. An algorithm based on co-regularization methods with extra penalty terms reflecting smoothness and general agreement properties is proposed. We first provide explicit tight control on the Rademacher (L1) complexity of the corresponding class of learners for arbitrary many views, then give the asymptotic behavior of the bounds when the coregularization term increases, making explicit the relation between consistency of the views and reduction of the search space. Since many views involve many parameters, we third provide a parameter selection procedure, based on the stability approach with clustering and localization arguments. To this aim, we give an explicit bound on the variance (L2- diameter) of the class of functions. Finally we illustrate the algorithm through simulations on toy examples.