Referenced Items are not available for this document.
In this paper, we face the problem of designing accurate decision-making modules in measurement systems that need to be implemented on resource-constrained platforms. We propose a methodology based on multiobjective optimization and genetic algorithms (GAs) for the analysis of support vector machine (SVM) solutions in the classification error-complexity space. Specific criteria for the choice of optimal SVM classifiers and experimental results on both real and synthetic data will also be discussed.
Published in:
Instrumentation and Measurement, IEEE Transactions on
(Volume:57
,
Issue:
9
)
Date of Publication: Sept. 2008
- Page(s):
- 2044 - 2051
- ISSN :
- 0018-9456
- INSPEC Accession Number:
- 10144013
- Digital Object Identifier :
- 10.1109/TIM.2008.917674
- Product Type:
- Journals & Magazines
- Date of Publication :
- 07 March 2008
- Date of Current Version :
- 05 August 2008
- Issue Date :
- Sept. 2008
- Sponsored by :
- IEEE Instrumentation and Measurement Society
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