Algorithmic Learning in a Random World (Hardcover, 2005 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	March 2005
First published:	2005
Authors:	Vladimir Vovk • Alex Gammerman • Glenn Shafer
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Hardcover
Pages:	324
Edition:	2005 ed.
ISBN-13:	978-0-387-00152-4
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	0-387-00152-2
Barcode:	9780387001524

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