Winnow (algorithm): Difference between revisions

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The '''winnow algorithm''' <ref>Littlestone, N. (1988) "Learning Quickly When Irrelevant Attributes AboutAbound: 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 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 learning setting, where the learning phase is not separated from the training phase.

==The algorithm==