Sequential predictions based on algorithmic complexity
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e. based on universal deterministic/one-part MDL. m is extremely close to Solomonoff’s universal 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...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Computer and System Sciences|
|Hutter et al Sequential Predictions based on Algorithmic Complexity 2006.pdf||357.29 kB||Adobe PDF|
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