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* If an example is correctly classified, do nothing.
* If an example is predicted incorrectly and the correct result was 0, for each feature <math>x_{i}=1</math>, the corresponding weight <math>w_{i}</math> is set to 0 (demotion step).
*: <math>\forall x_{i} = 1, w_{i} = 0</math>▼
* If an example is predicted incorrectly and the correct result was 1, for each feature <math>x_{i}=1</math>, the corresponding weight <math>w_{i}</math> multiplied by
*: <math>\forall x_{i} = 1, w_{i} = \alpha w_{i}</math>▼
A typical value for {{mvar|α}} is 2.▼
▲<math>\forall x_{i} = 1, w_{i} = 0</math>
There are many variations to this basic approach. ''Winnow2''<ref name="littlestone88"/> is similar except that in the demotion step the weights are divided by
▲* If an example is predicted incorrectly and the correct result was 1, for each feature <math>x_{i}=1</math>, the corresponding weight <math>w_{i}</math> multiplied by <math>\alpha</math> (promotion step).
▲<math>\forall x_{i} = 1, w_{i} = \alpha w_{i}</math>
▲A typical value for
▲There are many variations to this basic approach. ''Winnow2''<ref name="littlestone88"/> is similar except that in the demotion step the weights are divided by <math>\alpha</math> instead of being set to 0. ''Balanced Winnow'' maintains two sets of weights, and thus two hyperplanes. This can then be generalized for [[multi-label classification]].
==Mistake bounds==
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