Varieties of Regret in Online Prediction

نویسندگان

  • Casey Marks
  • Amy Greenwald
  • David Gondek
چکیده

We present a general framework for analyzing regret in the online prediction problem. We develop this from sets of linear transformations of strategies. We establish relationships among the varieties of regret and present a class of regret-matching algorithms. Finally we consider algorithms that exhibit the asymptotic no-regret property. Our main results are an analysis of observed regret in expectation and two regretmatching algorithms that exhibit no-observed-internal-regret in expectation.

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تاریخ انتشار 2004