CINECA IRIS Institutional Research Information System
IRIS è l’archivio della produzione scientifica della Fondazione Bruno Kessler.
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.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte di FBK.
| 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 |
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11582/60798
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
Copyright © 2018