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Springer Algorithmic Learning in a Random World

21514813

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£148.37 £74.42 £90.55 £106.69 £122.82 £138.96 £155.09 18 February 2026 12 March 2026 03 April 2026 25 April 2026 17 May 2026

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Description

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
22 March 2005
Listed Since
22 December 2006

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