Veness, Joel; Ng, Kee Siong; Hutter, Marcus; Bowling, Michael
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...[Show more]
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