Algorithmic Learning in a Random World

By: Vladimir Vovk, Alex Gammerman, Glenn Shafer

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Hardcover | 1 April 2005

At a Glance

Hardcover
344 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

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Published: 14th December 2022

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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.

Industry Reviews

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"Algorithmic Learning in a Random World has ten chapters, three appendices, and extensive references. Each chapter ends with a section containing comments, historical discussion, and bibliographical remarks. ... The material is developed well and reasonably easy to follow ... . the text is very readable. ... is doubtless an important reference summarizing a large body of work by the authors and their graduate students. Academics involved with new implementations and empirical studies of machine learning techniques may find it useful too." (James Law, SIGACT News, Vol. 37 (4), 2006)

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You Can Find This Book In

Non-Fiction Mathematics Algebra Probability & Statistics Computing & I.T.Computer Science Artificial Intelligence Machine Learning Computer Programming & Software Development Algorithms & Data Structures Business Applications Mathematical & Statistical Software

Preface
List of Principal results
Introduction
Conformal prediction
Classification with conformal predictors
Modifications of conformal predictors
Probabilistic prediction
impossibility results
Probabilistic prediction
Venn predictors
Beyond exchangeability
On-line compression modeling
conformal prediction
On-line compression modeling
Venn prediction
Perspectives and contrasts
Probability theory
Data sets
FAQ
Notation
References
Index
Table of Contents provided by Publisher. All Rights Reserved.

Algorithmic Learning in a Random World

At a Glance

New Edition

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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