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Fast Training of Support Vector Machines on the Cell Processor
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BibTeX
@MISC{Marzolla_fasttraining,
author = {Moreno Marzolla},
title = {Fast Training of Support Vector Machines on the Cell Processor},
year = {}
}
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Abstract
Support Vector Machines (SVMs) are a widely used technique for classification, clustering and data analysis. While efficient algorithms for training SVM are available, dealing with large datasets makes training and classification a computationally challenging problem. In this paper we exploit modern processor architectures to improve the training speed of LIBSVM, a well known implementation of the Sequential Minimal Optimization algorithm. We describe LIBSVMCBE, an optimized version of LIBSVM which takes advantage of the peculiar architecture of the Cell Broadband Engine. We assess the performance of LIBSVMCBE on real-world training problems, and we show how this optimization is particularly effective on large, dense datasets.
Keyphrases
support vector machine fast training cell processor real-world training problem data analysis cell broadband engine training svm efficient algorithm sequential minimal optimization training speed dense datasets large datasets modern processor architecture challenging problem peculiar architecture optimized version
