2 research outputs found
Online Learning with an Almost Perfect Expert
We study the multiclass online learning problem where a forecaster makes a
sequence of predictions using the advice of experts. Our main contribution
is to analyze the regime where the best expert makes at most mistakes and
to show that when , the expected number of mistakes made by
the optimal forecaster is at most . We also describe
an adversary strategy showing that this bound is tight and that the worst case
is attained for binary prediction