In this work, the active learning approach is adopted to address the problem of training sample collection for the estimation of chemical parameters for product quality control from spectroscopic data. In particular, two strategies for support vector regression (SVR) are proposed. The first method select samples distant in the kernel space from the current support vectors, while the second one uses a pool of regressors in order to choose the samples with the greater disagreements between the different regressors. The experimental results on two real data sets show the effectiveness of the proposed solutions. © 2012 IEEE.
SVR active learning for product quality control / Douak, F.; Melgani, F.; Pasolli, E.; Benoudjit, N.. - (2012), pp. 1113-1117. (Intervento presentato al convegno 2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 tenutosi a Montreal, QC, can nel 2012) [10.1109/ISSPA.2012.6310457].
SVR active learning for product quality control
Pasolli E.;
2012
Abstract
In this work, the active learning approach is adopted to address the problem of training sample collection for the estimation of chemical parameters for product quality control from spectroscopic data. In particular, two strategies for support vector regression (SVR) are proposed. The first method select samples distant in the kernel space from the current support vectors, while the second one uses a pool of regressors in order to choose the samples with the greater disagreements between the different regressors. The experimental results on two real data sets show the effectiveness of the proposed solutions. © 2012 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
