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SVM training time reduction using vector quantization | IEEE Conference Publication | IEEE Xplore

SVM training time reduction using vector quantization


Abstract:

In this paper, we describe a new method for training SVM on large data sets. Vector quantization is applied to reduce a large data set by replacing examples by prototypes...Show More

Abstract:

In this paper, we describe a new method for training SVM on large data sets. Vector quantization is applied to reduce a large data set by replacing examples by prototypes. Training time for choosing optimal parameters is greatly reduced. Some experimental results yields to demonstrate that this method can reduce training time by a factor of 100, while preserving classification rate. Moreover this method allows to find a decision function with a low complexity when the training data set includes noisy or error examples.
Date of Conference: 26-26 August 2004
Date Added to IEEE Xplore: 20 September 2004
Print ISBN:0-7695-2128-2
Print ISSN: 1051-4651
Conference Location: Cambridge, UK

References

References is not available for this document.