We study the properties of the MDL (or maximum penalized complexity) estimator for Regression and Classification, where the underlying model class is countable. We show in particular a finite bound on the Hellinger losses under the only assumption that there is a ``true'' model contained in the class. This implies almost sure convergence of the predictive distribution to the true one at a fast rate. It corresponds to Solomonoff's central theorem of universal induction, however with a bound that is exponentially larger
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
AbstractThis paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-logm,...
Solomonoff’s central result on induction is that the prediction of a universal semimeasure M converg...
Minimum Description Length (MDL) is an important principle for induction and prediction, with stron...
Minimum description length (MDL) is an important principle for induction and prediction, with strong...
We consider the Minimum Description Length principle for online sequence prediction. If the underlyi...
The Minimum Description Length principle for online sequence estimation/prediction in a proper learn...
We study the properties of the Minimum Description Length principle for sequence prediction, conside...
We study the properties of the Minimum Description Length principle for sequence prediction, conside...
We consider the Minimum Description Length principle for online sequence prediction. If the underlyi...
The Minimum Description Length principle for online sequence estimateion/prediction in a proper lear...
The Minimum Description Length (MDL) principle selects the model that has the shortest code for data...
In some estimation problems, especially in applications dealing with information theory, signal proc...
We present and relate recent results in prediction based on countable classes of either probability ...
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
AbstractThis paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-logm,...
Solomonoff’s central result on induction is that the prediction of a universal semimeasure M converg...
Minimum Description Length (MDL) is an important principle for induction and prediction, with stron...
Minimum description length (MDL) is an important principle for induction and prediction, with strong...
We consider the Minimum Description Length principle for online sequence prediction. If the underlyi...
The Minimum Description Length principle for online sequence estimation/prediction in a proper learn...
We study the properties of the Minimum Description Length principle for sequence prediction, conside...
We study the properties of the Minimum Description Length principle for sequence prediction, conside...
We consider the Minimum Description Length principle for online sequence prediction. If the underlyi...
The Minimum Description Length principle for online sequence estimateion/prediction in a proper lear...
The Minimum Description Length (MDL) principle selects the model that has the shortest code for data...
In some estimation problems, especially in applications dealing with information theory, signal proc...
We present and relate recent results in prediction based on countable classes of either probability ...
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
AbstractThis paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-logm,...
Solomonoff’s central result on induction is that the prediction of a universal semimeasure M converg...