On-line Learning and the Metrical Task System Problem

. The problem of combining expert advice, studied extensively in the Computational Learning Theory literature, and the Metrical Task System (MTS) problem, studied extensively in the area of On-line Algorithms, contain a number of interesting similarities. In this paper we explore the relationship between these problems and show how algorithms designed for each can be used to achieve good bounds and new approaches for solving the other. Specific contributions of this paper include: ffl An analysis of how two recent algorithms for the MTS problem can be applied to the problem of tracking the best expert in the "decision-theoretic" setting, providing good bounds and an approach of a much different flavor from the well-known multiplicative-update algorithms. ffl An analysis showing how the standard randomized Weighted Majority (or Hedge) algorithm can be used for the problem of "combining on-line algorithms on-line", giving much stronger guarantees than the results of Azar et al [1] when..