Winnow (algorithm): Difference between revisions

Revision as of 16:03, 15 September 2009

Revision as of 05:53, 5 October 2009

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The '''winnow algorithm'''<ref>Littlestone, N. (1988) "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm" Machine Learning 285-318(2)</ref> is a technique from [[machine learning]]. It is closely related to the [[perceptron]], but it uses a multiplicative weight-update scheme that allows it to perform much better than the perceptron 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]]. It can also be used in the [[Online machine learning|online learning]] setting, where the learning phase is not separated from the training phase.

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