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 contemporary theoretical and experimental advancements in construction computable approximations to Kolmogorov's algorithmic concept of randomness. in line with those approximations, a brand new set of desktop studying algorithms were constructed that may be used to make predictions and to estimate their self assurance and credibility in high-dimensional areas less than the standard assumption that the information are self sufficient and identically disbursed (assumption of randomness). one other objective of this distinct monograph is to stipulate a few limits of predictions: The method in keeping with algorithmic concept of randomness permits the evidence of impossibility of prediction in sure occasions. The e-book describes how a number of vital computing device studying difficulties, corresponding to density estimation in high-dimensional areas, can't be solved if the one assumption is randomness.

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