What does this research mean for the field?

The Context Tree Switching technique modifies Context Tree Weighting to mix over a strictly larger class of models without increasing asymptotic time or space complexity, yielding consistent empirical improvements in predicting binary, stationary, n-Markov sources. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

April 1, 2012

Context Tree Switching

Key Points

Key points are not available for this paper at this time.

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.

Bookmark

Cite This Study

Veness et al. (Sun,) studied this question.

synapsesocial.com/papers/6a07a125d343c0cd6cc63e5f https://doi.org/https://doi.org/10.1109/dcc.2012.39

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

Bookmark