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By Vladimir Vovk,Alex Gammerman,Glenn Shafer

Algorithmic studying in a Random World describes fresh theoretical and experimental advancements in development computable approximations to Kolmogorov's algorithmic proposal of randomness. in accordance with those approximations, a brand new set of desktop studying algorithms were built that may be used to make predictions and to estimate their self assurance and credibility in high-dimensional areas lower than the standard assumption that the knowledge are self reliant and identically dispensed (assumption of randomness). one other goal of this designated monograph is to stipulate a few limits of predictions: The procedure according to algorithmic thought of randomness permits the facts of impossibility of prediction in convinced occasions. The publication describes how numerous vital computer studying difficulties, reminiscent of density estimation in high-dimensional areas, can't be solved if the single assumption is randomness.

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Algorithmic Learning in a Random World by Vladimir Vovk,Alex Gammerman,Glenn Shafer


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