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Randomized weighted majority algorithm
The randomized weighted majority algorithm is an algorithm in machine learning theory.[1] It improves the mistake bound of the weighted majority algorithm.
Imagine that every morning before the stock market opens, we get a prediction from each of our "experts" about whether the stock market will go up or down. Our goal is to somehow combine this set of predictions into a single prediction that we then use to make a buy or sell decision for the day. The RWMA gives us a way to do this combination such that our prediction record will be nearly as good as that of the single best expert in hindsight.
Contributeurs: Imane Meziani, wiki
