Content deleted Content added
Slightly improved the introduction for non-specialists (but it is still not good) |
Specified that the examples from which the classifier is learned are labeled |
||
Line 1:
{{Context|date=October 2009}}
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]] 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, but winnow uses a multiplicative weight-update 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]]. It can also be used in the [[Online machine learning|online learning]] setting, where the learning phase is not separated from the training phase.
==The algorithm==
| |||