(One intermediate revision by one other user not shown)
Line 1:
The '''winnow algorithm'''<ref name="littlestone88"> Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm", [httphttps://wwwdoi.springerlinkorg/10.com/content/j0k7t38567325716/1023%2FA%3A1022869011914 ''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 Weight Update Method|multiplicative scheme]] that allows it to perform much better when many dimensions are irrelevant (hence its name [[winnowing|winnow]]). It is a simple algorithm that scales well to high-dimensional data. 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 algorithm can also be used in the [[Online machine learning|online learning]] setting, where the learning and the classification phase are not clearly separated.
