Winnow (algorithm)

The winnow algorithm is a technique from machine learning for learning a linear classifier from labeled examples. It is very similar to the perceptron algorithm. However, the perceptron algorithm uses an additive weight-update scheme, while Winnow uses a multiplicative scheme that allows it to perform much better when many dimensions are irrelevant (hence its name). 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 learning setting, where the learning and the classification phase are not clearly separated.

The basic algorithm, Winnow1, is as follows. The instance space is ${\displaystyle X=\{0,1\}^{n}}$, that is, each instance is described as a set of Boolean-valued features. The algorithm maintains non-negative weights ${\displaystyle w_{i}}$ for ${\displaystyle i\in \{1,\ldots ,n\}}$, which are initially set to 1, one weight for each feature. When the learner is given an example ${\displaystyle (x_{1},\ldots ,x_{n})}$, it applies the typical prediction rule for linear classifiers:

...
Wikipedia