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Second-Order Non-Stationary Online Learning for Regression
[article]
2013
arXiv
pre-print
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to have this gap arbitrarily close to zero, compared with the best function that is chosen off-line. Nevertheless, many real-world applications, such as adaptive filtering, are non-stationary in nature, and the best prediction function may drift over time. We introduce two novel algorithms for online
arXiv:1303.0140v1
fatcat:6vmvxlpq3narxaavbndragrlie