Algorithmic learning in a random world

by Vladimir Vovk, Alex Gammerman, and Glenn Shafer

New York: Springer, 2005

Click to enlarge The main topic of this book is conformal prediction, a method of prediction recently developed in machine learning. Conformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accuracy and reliability.

The book integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.

Topics and Features:

Researchers in computer science, statistics, and artificial intelligence will find the book an authoritative and rigorous treatment of some of the most promising new developments in machine learning. Practitioners and students in all areas of research that use quantitative prediction or machine learning will learn about important new methods.

The book may be purchased directly from Springer and from many on-line booksellers, including

Vladimir Vovk and Alex Gammerman are Professors of Computer Science at Royal Holloway, University of London. Glenn Shafer is Professor in the Rutgers School of Business - Newark and New Brunswick. All three authors are affiliated with the Centre for Reliable Machine Learning at Royal Holloway, University of London.

On-line Compression Modelling Project (New Series)

The following articles further develop and review the theory presented in the book:

For the old working papers, mainly superseded by the book, follow this link.

For other research on conformal prediction, see the on-line prediction wiki. See also the events page.

This page is maintained by Vladimir Vovk.   Last modified on 6 April 2021