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https://hdl.handle.net/2440/30009
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Type: | Book chapter |
Title: | An implementation of training dual-nu support vector machines |
Author: | Chew, H. Lim, C. Bogner, R. |
Citation: | Applied optimization - Optimization and control with applications, 2005 / Qi, L., Teo, K., Yang, X. (ed./s), vol.96, pp.157-182 |
Publisher: | Springer |
Publisher Place: | New York, USA |
Issue Date: | 2005 |
Series/Report no.: | Applied optimization ; 96 |
ISBN: | 0387242546 |
Editor: | Qi, L. Teo, K. Yang, X. |
Statement of Responsibility: | Hong-Gunn Chew, Cheng-Chew Lim and Robert E. Bogner |
Abstract: | Dual-ν Support Vector Machine (2ν-SVM) is a SVM extension that reduces the complexity of selecting the right value of the error parameter selection. However, the techniques used for solving the training problem of the original SVM cannot be directly applied to 2ν-SVM. An iterative decomposition method for training this class of SVM is described in this chapter. The training is divided into the initialisation process and the optimisation process, with both processes using similar iterative techniques. Implementation issues, such as caching, which reduces the memory usage and redundant kernel calculations are discussed. |
Description: | The original publication is available at www.springerlink.com |
DOI: | 10.1007/0-387-24255-4_7 |
Published version: | http://www.springerlink.com/content/l157014rr76j1j5p/ |
Appears in Collections: | Aurora harvest 2 Electrical and Electronic Engineering publications |
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