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Traditionally, proofs of universal consistency of particular machine learning algorithms – including local learning algorithms such as k-NN classifier – are given under the assumption of data inputs being independent identically distributed random variables. This assumption is often too strong, for instance, when modelling learning and generalization of time series. A sequence of random variables […]
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