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

By Vladimir Vovk,Alex Gammerman,Glenn Shafer

Algorithmic studying in a Random World describes fresh theoretical and experimental advancements in construction computable approximations to Kolmogorov's algorithmic suggestion of randomness. in response to those approximations, a brand new set of desktop studying algorithms were constructed that may be used to make predictions and to estimate their self belief and credibility in high-dimensional areas less than the standard assumption that the information are self reliant and identically dispensed (assumption of randomness). one other target of this exact monograph is to stipulate a few limits of predictions: The technique in accordance with algorithmic thought of randomness makes it possible for the evidence of impossibility of prediction in convinced occasions. The publication describes how numerous very important computer studying difficulties, reminiscent of density estimation in high-dimensional areas, can't be solved if the one assumption is randomness.

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