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ICANN
2001
Springer

Fast Training of Support Vector Machines by Extracting Boundary Data

13 years 8 months ago
Fast Training of Support Vector Machines by Extracting Boundary Data
Support vector machines have gotten wide acceptance for their high generalization ability for real world applications. But the major drawback is slow training for classification problems with a large number of training data. To overcome this problem, in this paper, we discuss extracting boundary data from the training data and train the support vector machine using only these data. Namely, for each training datum we calculate the Mahalanobis distances and extract those data that are misclassified by the Mahalanobis distances or that have small relative differences of the Mahalanobis distances. We demonstrate the effectiveness of the method for the benchmark data sets.
Shigeo Abe, Takuya Inoue
Added 29 Jul 2010
Updated 29 Jul 2010
Type Conference
Year 2001
Where ICANN
Authors Shigeo Abe, Takuya Inoue
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