Context Tree Switching
Information Theory
2011-11-15 v1 math.IT
Abstract
This paper describes the Context Tree Switching technique, a modification of Context Tree Weighting for the prediction of binary, stationary, n-Markov sources. By modifying Context Tree Weighting's recursive weighting scheme, it is possible to mix over a strictly larger class of models without increasing the asymptotic time or space complexity of the original algorithm. We prove that this generalization preserves the desirable theoretical properties of Context Tree Weighting on stationary n-Markov sources, and show empirically that this new technique leads to consistent improvements over Context Tree Weighting as measured on the Calgary Corpus.
Keywords
Cite
@article{arxiv.1111.3182,
title = {Context Tree Switching},
author = {Joel Veness and Kee Siong Ng and Marcus Hutter and Michael Bowling},
journal= {arXiv preprint arXiv:1111.3182},
year = {2011}
}
Comments
Technical Report