Difference between revisions of "Winnow Algorithm"
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Winnow Algorithm is a [[category::method | ]] for learning a linear classifier/decision hyper-plane from labeled examples. It scales well to high dimensions especially when many of the dimensions are irrelevant. Thus it finds good use for text classification problems using a bag-of-words feature representation. | Winnow Algorithm is a [[category::method | ]] for learning a linear classifier/decision hyper-plane from labeled examples. It scales well to high dimensions especially when many of the dimensions are irrelevant. Thus it finds good use for text classification problems using a bag-of-words feature representation. | ||
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| + | == Relevant Papers == | ||
| + | # [http://www.springerlink.com/content/j0k7t38567325716/?MUD=MP Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm", Machine Learning] | ||
| + | # [Littlestone, Nicholas. "Mistake bounds and logarithmic linear-threshold learning algorithms." (1990).] | ||
Revision as of 06:08, 6 November 2012
Winnow Algorithm is a for learning a linear classifier/decision hyper-plane from labeled examples. It scales well to high dimensions especially when many of the dimensions are irrelevant. Thus it finds good use for text classification problems using a bag-of-words feature representation.
Relevant Papers
- Nick Littlestone (1988). "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm", Machine Learning
- [Littlestone, Nicholas. "Mistake bounds and logarithmic linear-threshold learning algorithms." (1990).]
