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 MoreMetadata
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.
Published in: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
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