Abstract
We present data-dependent error bounds for transductive learning based on transductive Rademacher complexity. For specific algorithms we provide bounds on their Rademacher complexity based on their "unlabeled-labeled" decomposition. This decomposition technique applies to many current and practical graph-based algorithms. Finally, we present a new PAC-Bayesian bound for mixtures of transductive algorithms based on our Rademacher bounds. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
El-Yaniv, R., & Pechyony, D. (2007). Transductive rademacher complexity and its applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4539 LNAI, pp. 157–171). Springer Verlag. https://doi.org/10.1007/978-3-540-72927-3_13
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