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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El-Yaniv, R., Pechyony, D. (2007). Transductive Rademacher Complexity and Its Applications. In: Bshouty, N.H., Gentile, C. (eds) Learning Theory. COLT 2007. Lecture Notes in Computer Science(), vol 4539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72927-3_13
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DOI: https://doi.org/10.1007/978-3-540-72927-3_13
Publisher Name: Springer, Berlin, Heidelberg
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