107 search results - page 1 / 22 » General Loss Bounds for Universal Sequence Prediction |
ICML
2001
IEEE
14 years 5 months ago
2001
IEEE
The Bayesian framework is ideally suited for induction problems. The probability of observing xt at
TIT
1998
13 years 4 months ago
1998
Abstract— We consider adaptive sequential prediction of arbitrary binary sequences when the performance is evaluated using a general loss function. The goal is to predict on each...
TIT
2002
13 years 4 months ago
2002
We consider the problem of sequential linear prediction of real-valued sequences under the square-error loss function. For this problem, a prediction algorithm has been demonstrate...
CORR
2007
Springer
13 years 4 months ago
2007
Springer
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true d...
ALT
2005
Springer
14 years 1 months ago
2005
Springer
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true ...