This is an important family of algorithms in Competitive On-line Prediction. At each trial the weight of each strategy in the benchmark class is multiplied by $e^{-\eta l}$, where $\eta$ is a constant called the learning rate and $l$ is the strategy's loss. The master's prediction is obtained as a weighted average (in different senses) of the strategies' predictions.