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Learning from non-IID data: Theory, Algorithms and Practice

ECML 2009 LNIID Workshop

7 September 2009, Bled, Slovenia

http://www-connex.lip6.fr/~amini/ecml-wk-lniid.html

(proceedings available on-line)

Ingo Steinwart, Andreas Christmann

Los Alamos National Laboratory; University of Bayreuth

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

Abstract : We prove an oracle inequality for generic regularized empirical risk minimization algorithms learning from $\a$-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.

http://nips.cc/Conferences/2009/Program/event.php?ID=1778

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