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{{Short description|Method of using a pool of algorithms}}
In [[machine learning]], '''weighted majority algorithm (WMA)''' is a [[meta-learning (computer science)|meta learning]] [[algorithm]] used to construct a compound algorithm from a [[Pool (computer science)|pool]] of prediction algorithms, which could be any type of learning algorithms, classifiers, or even real human experts.<ref name="LW94">{{cite journal▼
▲In [[machine learning]], '''weighted majority algorithm (WMA)''' is a [[meta-learning (computer science)|meta learning]] [[algorithm]] used to construct a compound algorithm from a pool of prediction algorithms, which could be any type of learning algorithms, classifiers, or even real human experts.<ref name="LW94">{{cite journal
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| journal = Information and Computation
| volume = 108
| issue = 2
| date = 1994
| pages = 212–261
| doi=10.1006/inco.1994.1009
| url = http://www.dklevine.com/archive/refs4575.pdf
}}</ref><ref name="LW89">{{cite conference
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Assume that the problem is a binary [[decision problem]]. To construct the compound algorithm, a positive weight is given to each of the algorithms in the pool. 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 and lower bounds|upper bounds]] on the number of mistakes made in a given sequence of predictions from a pool of algorithms <math> \mathbf{A} </math> is
:<math>\mathbf{O(log|A|+m)}</math>
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