Algorithmic Learning in a Random World

Vladimir Vovk, Alex Gammerman, Glenn Shafer

340 pages, parution le 01/06/2004

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Résumé

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.

Written for: Researchers, professionals

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Sommaire

List of Principal results
Introduction
Conformal prediction
Classification with conformal predictors
Modifications of conformal predictors
Probabilistic prediction I: impossibility results
Probabilistic prediction II: Venn predictors
Beyond exchangeability
On-line compression modeling I: conformal prediction
On-line compression modeling II: Venn prediction
Perspectives and contrasts

Appendix A: Probability theory
Appendix B: Data sets
Appendix C: FAQ

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Vladimir Vovk, Alex Gammerman, Glenn Shafer
Parution	01/06/2004
Nb. de pages	340
Format	16 x 24
Couverture	Relié
Poids	690g
Intérieur	Noir et Blanc
EAN13	9780387001524
ISBN13	978-0-387-00152-4