Algorithmic Learning in a Random World

Name: Algorithmic Learning in a Random World
SKU: 2113208
Price: 537.00 NZD
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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.

Release date NZ

March 22nd, 2005

Author

Audiences

Postgraduate, Research & Scholarly
Professional & Vocational
Undergraduate

Edition

2005 ed.

Illustrations

62 Illustrations, black and white; XVI, 324 p. 62 illus.

Pages

324

Dimensions

156x234x20

ISBN-13

9780387001524

Product ID

2113208

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