Matrix analysis
The nature of statistical learning theory
The nature of statistical learning theory
PAC-Bayesian Stochastic Model Selection
Machine Learning
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
ICML '05 Proceedings of the 22nd international conference on Machine learning
Effective transductive learning via objective model selection
Pattern Recognition Letters
An analysis of graph cut size for transductive learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Complexity of pattern classes and the Lipschitz property
Theoretical Computer Science
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning
The Journal of Machine Learning Research
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Graph-Based Semi-Supervised Learning and Spectral Kernel Design
IEEE Transactions on Information Theory
Maximum volume clustering: a new discriminative clustering approach
The Journal of Machine Learning Research
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We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive Rademacher complexity. Our technique is based on a novel general error bound for transduction in terms of transductive Rademacher complexity, together with a novel bounding technique for Rademacher averages for particular algorithms, in terms of their "unlabeled-labeled" representation. This technique is relevant to many advanced graph-based transductive algorithms and we demonstrate its effectiveness by deriving error bounds to three well known algorithms. Finally, we present a new PAC-Bayesian bound for mixtures of transductive algorithms based on our Rademacher bounds.