Abstract
This paper reviews some theoretical and experimental developments in building computable approximations of Kolmogorov's algorithmic notion of randomness. Based on these approximations a new set of machine learning algorithms have been developed that can be used not just to make predictions but also to estimate the confidence under the usual iid assumption.
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