On the Convergence Speed of MDL Predictions for Bernoulli Sequences
We consider the Minimum Description Length principle for online sequence prediction. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is bounded, implying convergence with probability one, and (b) it additionally specifies a rate of convergence. Generally, for MDL only exponential loss bounds hold, as opposed to the linear bounds for a Bayes mixture. We show that this is even the case if the model...[Show more]
|Collections||ANU Research Publications|
|Source:||Algorithmic Learning Theory: 15th International Conference, ALT 2004, Pedova, Italy, October 2004, Proceedings|
|01_Poland_On_the_Convergence_Speed_of_2004.pdf||351.16 kB||Adobe PDF||Request a copy|
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