Approximation of the Two-Part MDL Code
Machine Learning
2008-09-15 v2 Artificial Intelligence
Information Theory
math.IT
Abstract
Approximation of the optimal two-part MDL code for given data, through successive monotonically length-decreasing two-part MDL codes, has the following properties: (i) computation of each step may take arbitrarily long; (ii) we may not know when we reach the optimum, or whether we will reach the optimum at all; (iii) the sequence of models generated may not monotonically improve the goodness of fit; but (iv) the model associated with the optimum has (almost) the best goodness of fit. To express the practically interesting goodness of fit of individual models for individual data sets we have to rely on Kolmogorov complexity.
Cite
@article{arxiv.cs/0612095,
title = {Approximation of the Two-Part MDL Code},
author = {Pieter Adriaans and Paul Vitanyi},
journal= {arXiv preprint arXiv:cs/0612095},
year = {2008}
}
Comments
14 pages, LaTeX, no figures, IEEE Trans Inform. Th., to appear