ML Anthology

Transductive Rademacher Complexity and Its Applications

Ran El-Yaniv, Dmitry Pechyony
COLT 2007 pp. 157-171
doi:10.1007/978-3-540-72927-3_13 /colt/2007/elyaniv2007colt-transductive/

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.

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Text

El-Yaniv and Pechyony. "Transductive Rademacher Complexity and Its Applications." Annual Conference on Computational Learning Theory, 2007. doi:10.1007/978-3-540-72927-3_13

Markdown

[El-Yaniv and Pechyony. "Transductive Rademacher Complexity and Its Applications." Annual Conference on Computational Learning Theory, 2007.](https://mlanthology.org/colt/2007/elyaniv2007colt-transductive/) doi:10.1007/978-3-540-72927-3_13

BibTeX

@inproceedings{elyaniv2007colt-transductive,
  title     = {{Transductive Rademacher Complexity and Its Applications}},
  author    = {El-Yaniv, Ran and Pechyony, Dmitry},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2007},
  pages     = {157-171},
  doi       = {10.1007/978-3-540-72927-3_13},
  url       = {https://mlanthology.org/colt/2007/elyaniv2007colt-transductive/}
}