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We present data-dependent learning bounds for the general scenario of non-stationary non-mixing stochastic processes.
Atmospheric predictability as revealed by naturally occurring analogues
E. N. Lorenz · 1969
Earlier work this paper cites.
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Robert Engle · 1982
Earlier work this paper cites.
First order autoregressive processes and strong mixing
Donald Andrews · 1983
Earlier work this paper cites.
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev · 1986
Earlier work this paper cites.
Time Series: Theory and Methods
Peter J Brockwell and Richard A Davis · 1986
Earlier work this paper cites.
Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
Nick Littlestone · 1987
Earlier work this paper cites.
Time Series Analysis, Forecasting and Control
George Edward Pelham Box and Gwilym Jenkins · 1990
Earlier work this paper cites.
Probability in Banach Spaces: Isoperimetry and Processes
M. Ledoux and M. Talagrand · 1991
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
On the complexity of learning from drifting distributions
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Earlier work this paper cites.
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New analysis and algorithm for learning with drifting distributions
Mehryar Mohri and Andres Muñoz Medina · 2012
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Foundations of Machine Learning
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