Statistical learning based on Markovian data maximal deviation inequalities and learning rates
Author:
Funder
Polish National Science Center
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Artificial Intelligence
Link
http://link.springer.com/content/pdf/10.1007/s10472-019-09670-6.pdf
Reference42 articles.
1. Adamczak, R., Bednorz, W.: Exponential concentration inequalities for additive functionals of Markov chains. ESAIM: PS 19, 440–481 (2015)
2. Adams, T.M., Nobel, A.B.: Uniform convergence of Vapnik-Chervonenkis classes under ergodic sampling. Ann. Probab. 38, 1345–1367 (2010)
3. Agarwal, A., Duchi, J.: The generalization ability of online algorithms for dependent data. IEEE Trans. Inf. Theory 59(1), 573–587 (2013)
4. Alquier, P., Wintenberger, O.: Model selection for weakly dependent time series forecasting. Bernoulli 18, 883–913 (2012)
5. Asmussen, S.: Applied probability and queues. Springer, New York (2003)
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