Sequence Prediction based on Monotone 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 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...[Show more]
|Collections||ANU Research Publications|
|Source:||Computational Learning Theory and Kernel Machines, 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington DC, USA, August 24-27, 2003|
|01_Hutter_Sequence_Prediction_based_on_2003.pdf||1.1 MB||Adobe PDF||Request a copy|
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