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The randomized weighted majority algorithm is an algorithm in machine learning theory. 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. == Motivation == In machine learning, the weighted majority algorithm (WMA) is a meta-learning algorithm which "predicts from expert advice". It is not a randomized algorithm: initialize all experts to weight 1. for each round: poll all the experts and predict based on a weighted majority vote of their predictions. cut in half the weights of all experts that make a mistake. Suppose there are experts and the best expert makes mistakes. The weighted majority algorithm (WMA) makes at most mistakes, which is not a very good bound. We can do better by introducing randomization. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Randomized weighted majority algorithm」の詳細全文を読む スポンサード リンク
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