On the use of randomized experts in sequential strategies for loss functions with memory

The sequential decision problem for loss functions with memory is extended to cover the infinite class of randomized finite-state reference strategies. After showing the necessity of this extension, an on-line strategy is devised for which the normalized "regret" over an arbitrary sequence of observations of length n is O([(ln n)/n]/sup 1/3/).