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AAAI
2006

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Closest Pairs Data Selection for Support Vector Machines

14 years 9 days ago

Closest Pairs Data Selection for Support Vector Machines

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This paper presents data selection procedures for support vector machines (SVM). The purpose of data selection is to reduce the dataset by eliminating as many non support vectors (non-SVs) as possible. Based on the fact that support vectors (SVs) are those vectors close to the decision boundary, data selection keeps only the closest pair vectors of opposite classes. The selected dataset will replace the full dataset as the training component for any standard SVM algorithm.

Chaofan Sun

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AAAI 2006 | Data Selection | Intelligent Agents | Non Support Vectors | Support Vectors |

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Added	30 Oct 2010
Updated	30 Oct 2010
Type	Conference
Year	2006
Where	AAAI
Authors	Chaofan Sun

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