2003 | OriginalPaper | Buchkapitel
Sequence Prediction Based on Monotone Complexity
verfasst von : Marcus Hutter
Erschienen in: Learning Theory and Kernel Machines
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − logm, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff’s prior M, the latter being an excellent predictor in deterministic as well as probabilistic environments, where performance is measured in terms of convergence of posteriors or losses. Despite this closeness to M, it is difficult to assess the prediction quality of m, since little is known about the closeness of their posteriors, which are the important quantities for prediction. We show that for deterministic computable environments, the “posterior” and losses of m converge, but rapid convergence could only be shown on-sequence; the off-sequence behavior is unclear. In probabilistic environments, neither the posterior nor the losses converge, in general.