Winnow (algorithm)

The winnow algorithm 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.

The update rule is (loosely):

• If an example is correctly classified, do nothing.
• If an example is incorrectly classified, double or halve all weights involved in the mistake.

Variations are also used.