Consistency of discrete Bayesian learning
Theoretical Computer Science
Sequential predictions based on algorithmic complexity
Journal of Computer and System Sciences
Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework
Journal of Visual Communication and Image Representation
The safe bayesian: learning the learning rate via the mixability gap
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
| Hi-index | 754.84 |
The authors introduce an index of resolvability that is proved to bound the rate of convergence of minimum complexity density estimators as well as the information-theoretic redundancy of the corresponding total description length. The results on the index of resolvability demonstrate the statistical effectiveness of the minimum description-length principle as a method of inference. The minimum complexity estimator converges to true density nearly as fast as an estimator based on prior knowledge of the true subclass of densities. Interpretations and basic properties of minimum complexity estimators are discussed. Some regression and classification problems that can be examined from the minimum description-length framework are considered