Chernov, Alexey ;
Hutter, Marcus ;
Schmidhuber, Jürgen
Complexity Monotone in Conditions and Future Prediction Errors
Abstract
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor $M$ from the true distribution $mu$ by the algorithmic complexity of $mu$. Here we assume we are at a time $t>1$ and already observed $x=x_1...x_t$. We bound the future prediction performance on $x_{t+1}x_{t+2}...$ by a new variant of algorithmic complexity of $mu$ given $x$, plus the complexity of the randomness deficiency of $x$. The new
complexity is monotone in its condition in the sense that this complexity can only decrease if the condition is prolonged. We also briefly discuss potential generalizations to Bayesian model classes and to classification problems.
BibTeX  Entry
@InProceedings{chernov_et_al:DSP:2006:632,
author = {Alexey Chernov and Marcus Hutter and J{\"u}rgen Schmidhuber},
title = {Complexity Monotone in Conditions and Future Prediction Errors},
booktitle = {Kolmogorov Complexity and Applications},
year = {2006},
editor = {Marcus Hutter and Wolfgang Merkle and Paul M.B. Vitanyi},
number = {06051},
series = {Dagstuhl Seminar Proceedings},
ISSN = {18624405},
publisher = {Internationales Begegnungs und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2006/632},
annote = {Keywords: Kolmogorov complexity, posterior bounds, online sequential prediction, Solomonoff prior, monotone conditional complexity, total error, future loss, ra}
}
2006
Keywords: 

Kolmogorov complexity, posterior bounds, online sequential prediction, Solomonoff prior, monotone conditional complexity, total error, future loss, ra 
Seminar: 

06051  Kolmogorov Complexity and Applications

Related Scholarly Article: 


Issue date: 

2006 
Date of publication: 

2006 