Veness, JoelNg, Kee SiongHutter, MarcusBowling, Michael2015-08-172015-08-17978-1-4673-0715-4http://hdl.handle.net/1885/14741This 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.© 2012 IEEE. Authors can archive accepted version. http://www.ieee.org/publications_standards/publications/rights/rights_policies.html as at 17/08/15© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works201210.1109/DCC.2012.39