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Fast training of support vector machines on the Cell processor
- Source :
-
Neurocomputing . Oct2011, Vol. 74 Issue 17, p3700-3707. 8p. - Publication Year :
- 2011
-
Abstract
- 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 , a well known implementation of the sequential minimal optimization algorithm. We describe , an optimized version of which takes advantage of the peculiar architecture of the Cell Broadband Engine. We assess the performance of on real-world training problems, and we show how this optimization is particularly effective on large, dense datasets. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 74
- Issue :
- 17
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 65496826
- Full Text :
- https://doi.org/10.1016/j.neucom.2011.04.011