Metric entropy in competitive on-line prediction. / Vovk, Vladimir.

2006.

Research output: Working paper

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Abstract

Competitive on-line prediction (also known as universal prediction of individual sequences) is a strand of learning theory avoiding making anystochastic assumptions about the way the observations are generated. The predictor's goal is to compete with a benchmark class of prediction rules, which is often a proper Banach function space. Metric entropy provides a unifying framework for competitive on-line prediction: the numerous known upper bounds on the metric entropy of various compact sets in function spaces readily imply bounds on the performance of on-line prediction strategies. This paper discusses strengths and limitations of the direct approach to competitive on-line prediction via metric entropy, including comparisons to other approaches.
Original languageEnglish
Publication statusPublished - 9 Sep 2006
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 934712