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EnglishIn 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.
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| Titolo: | Accurate and resource-aware classification based on measurement data |
| Autori: | |
| Data di pubblicazione: | 2008 |
| Rivista: | |
| Abstract: | 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. |
| Handle: | http://hdl.handle.net/11582/60798 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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http://hdl.handle.net/11582/60798
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