We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
Abstract. We study online regret minimization algorithms in a bicriteria setting, examining not only the standard notion of regret to the best expert, but also the regret to the av...
Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, ...
We show how models for prediction with expert advice can be defined concisely and clearly using hidden Markov models (HMMs); standard HMM algorithms can then be used to efficientl...
This paper deals with the problem of making predictions in the online mode of learning where the dependence of the outcome yt on the signal xt can change with time. The Aggregating...
Abstract. In the biometric field, different experts are combined to improve the system reliability, as in many application the performance attained by individual experts (i.e., d...