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Fast Learning from Non-i.i.d. Observations

Ingo Steinwart, Andreas Christmann

Advances in Neural Information Processing Systems (NeurIPS), pp. 1768–1776, 2009.


We prove an oracle inequality for generic regularized empirical risk minimization algorithms learning from α-mixing processes. To illustrate this oracle inequality, we use it to derive learning rates for some learning methods including least squares SVMs. Since the proof of the oracle inequality uses recent localization ideas developed for independent and identically distributed (i.i.d.) processes, it turns out that these learning rates are close to the optimal rates known in the i.i.d. case.



@inproceedings{steinwart09_neurips, title = {Fast Learning from Non-i.i.d. Observations}, author = {Steinwart, Ingo and Christmann, Andreas}, year = {2009}, booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, volume = {22}, pages = {1768--1776}, url = {https://papers.nips.cc/paper/2009/hash/a89cf525e1d9f04d16ce31165e139a4b-Abstract.html} }