| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1181499 | Chemometrics and Intelligent Laboratory Systems | 2012 | 7 Pages |
Abstract
⺠We propose a new semi-supervised support vector machine ( S3VM) formulation. ⺠We identify the hardness of licorice seeds employing S3VM and NIR spectral data. ⺠DC programming is used to solve the resulting model. ⺠The proposed method converges quickly and has low computational burden. ⺠Experimental results show that our S3VM outperforms the existing methods.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Liming Yang, Qun Sun,