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 keeping with those approximations, a brand new set of computer 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 information are self sufficient and identically disbursed (assumption of randomness). one other objective of this precise monograph is to stipulate a few limits of predictions: The procedure in line with algorithmic idea of randomness allows the evidence of impossibility of prediction in sure events. The publication describes how a number of very important laptop studying difficulties, similar to density estimation in high-dimensional areas, can't be solved if the single assumption is randomness.

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