Exponential Weights Algorithms
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.
Algorithms in this class include: