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Worst Case Prediction over Sequences under Log Loss

  • Manfred Opper
  • David Haussler
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 107)

Abstract

We consider the game of sequentially assigning probabilities to future data based on past observations under logarithmic loss. We are not making probabilistic assumptions about the generation of the data, but consider a situation where a player tries to minimize his loss relative to the loss of the (with hindsight) best distribution from a target class for the worst sequence of data. We give bounds on the minimax regret in terms of the metric entropies of the target class with respect to suitable distances between distributions.

Keywords

Minimax Regret Computational Learning Theory Probabilistic Assumption Target Family Good Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Manfred Opper
    • 1
  • David Haussler
    • 2
  1. 1.Institut für Theoretische Physik IIIUniversität WürzburgWürzburgGermany
  2. 2.Department of Computer and Information SciencesUniversity of CaliforniaSanta CruzGermany

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