Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISSPA.2012.6310457 | Information Science, Signal Processing and their Applications |
Keywords | Field | DocType |
chemical engineering,learning (artificial intelligence),product quality,quality control,regression analysis,spectroscopy,support vector machines,SVR active learning,chemical parameters,kernel space,product quality control,support vector regression,training sample collection,Active learning,chemical parameter estimation,product quality control,spectroscopy,support vector regression (SVR) | Kernel (linear algebra),Data mining,Data set,Active learning,Pattern recognition,Least squares support vector machine,Computer science,Regression analysis,Support vector machine,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-0380-4 | 2 | 0.40 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fouzi Douak | 1 | 2 | 0.40 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Edoardo Pasolli | 3 | 285 | 17.04 |
| Nabil Benoudjit | 4 | 69 | 6.10 |