Metric entropy in competitive on-line prediction
<|reference_start|>Metric entropy in competitive on-line prediction: Competitive on-line prediction (also known as universal prediction of individual sequences) is a strand of learning theory avoiding making any stochastic 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.<|reference_end|>
@article{vovk2006metric,
    title={Metric entropy in competitive on-line prediction},
    author={Vladimir Vovk},
    journal={arXiv preprint arXiv:cs/0609045},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0609045},
    primaryClass={cs.LG}
}