[1][2] The algorithm assumes that we have no prior knowledge about the accuracy of the algorithms in the pool, but there are sufficient reasons to believe that one or more will perform well.
The compound algorithm then collects weighted votes from all the algorithms in the pool, and gives the prediction that has a higher vote.
If the compound algorithm makes a mistake, the algorithms in the pool that contributed to the wrong predicting will be discounted by a certain ratio β where 0<β<1.
It can be shown that the upper bounds on the number of mistakes made in a given sequence of predictions from a pool of algorithms
There are many variations of the weighted majority algorithm to handle different situations, like shifting targets, infinite pools, or randomized predictions.