Transductive confidence machine is universal

Ilia Nouretdinov, Vladimir V’yugin, Alex Gammerman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Vovk’s Transductive Confidence Machine (TCM) is a practical prediction algorithm giving, in additions to its predictions, confidence information valid under the general iid assumption. The main result of this paper is that the prediction method used by TCM is universal under a natural definition of what “valid” means: any prediction algorithm providing valid confidence information can be replaced, without losing much of its predictive performance, by a TCM. We use as the main tool for our analysis the Kolmogorov theory of complexity and algorithmic randomness.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 14th International Conference, ALT 2003, Proceedings
EditorsRicard Gavalda, Klaus P. Jantke, Eiji Takimoto
PublisherSpringer Verlag
Pages283-297
Number of pages15
ISBN (Print)3540202919, 9783540202912
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event14th International Conference on Algorithmic Learning Theory, ALT 2003 - Sapporo, Japan
Duration: 17 Oct 200319 Oct 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2842
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Algorithmic Learning Theory, ALT 2003
Country/TerritoryJapan
CitySapporo
Period17/10/0319/10/03

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