Support Vector Regression in Spectrophotometry: An Experimental Study
| dc.contributor.author | Fouzi Douak | |
| dc.date.accessioned | 2024-02-13T21:43:28Z | |
| dc.date.available | 2024-02-13T21:43:28Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | In this work, we present a detailed experimental assessment of an interesting regression approach based on support vector machines (SVMs), a technique relatively recently introduced in the literature. The experimental framework reports a thorough investigation of the performance of SVMs from different viewpoints, including: (i) the influence of the kernel type in the SVM regression task; (ii) the sensitivity to the number of input variables (spectra dimension); (iii) the sensitivity to the available number of training samples; and (iv) the overall stability. The obtained results are compared with those yielded by the radial basis function (RBF) and the multilayer perceptron (MLP) neural networks as well as the traditional multiple linear regression (MLR) method on two different spectrophotometric datasets. | |
| dc.identifier.uri | http://dspace.univ-khenchela.dz:4000/handle/123456789/823 | |
| dc.language.iso | en | |
| dc.publisher | Taylor and Francis - Critical Reviews in Analytical Chemistry | |
| dc.title | Support Vector Regression in Spectrophotometry: An Experimental Study | |
| dc.type | Article |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- 2012_Support Vector Regression in Spectrophotometry An Experimental Study.pdf
- Size:
- 25.57 KB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed to upon submission
- Description: