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The '''winnow algorithm'''<ref name="littlestone88">
Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm", ''[http://www.springerlink.com/content/j0k7t38567325716/ Machine Learning 285–318(2)].</ref> is a technique from [[machine learning]] for learning a [[linear classifier]] from labeled examples. It is very similar to the [[perceptron|perceptron algorithm]]. However, the perceptron algorithm uses an additive weight-update scheme, while Winnow uses a multiplicative scheme that allows it to perform much better when many dimensions are irrelevant (hence its name). It is not a sophisticated algorithm but it scales well to high-dimensional spaces. During training, Winnow is shown a sequence of positive and negative examples. From these it learns a decision [[hyperplane]] that can then be used to label novel examples as positive or negative. The basic technique has been one of the most successful in creating context-sensitive [[spell checkers]]. The algorithm can also be used in the [[Online machine learning|online learning]] setting, where the learning and the classification phase are not clearly separated.
==The algorithm==
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