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Reducing examples to accelerate support vector regression

โœ Scribed by Gao Guo; Jiang-She Zhang

Book ID
103879563
Publisher
Elsevier Science
Year
2007
Tongue
English
Weight
421 KB
Volume
28
Category
Article
ISSN
0167-8655

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โœฆ Synopsis

With increasing of the number of training examples, training time for support vector regression machine augments greatly. In this paper we develop a method to cut the training time by reducing the number of training examples based on the observation that support vector's target value is usually a local extremum or near extremum. The proposed method first extracts extremal examples from the full training set, and then the extracted examples are used to train a support vector regression machine. Numerical results show that the proposed method can reduce training time of support regression machine considerably and the obtained model has comparable generalization capability with that trained on the full training set.

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