The nature of statistical learning theory
The nature of statistical learning theory
Effect of number of features on classification of roller bearing faults using SVM and PSVM
Expert Systems with Applications: An International Journal
A novel SVM+NDA model for classification with an application to face recognition
Pattern Recognition
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Hyperspectral scattering images between 600nm and 1000nm were acquired for 580 'Delicious' apples for mealiness classification. A locally linear embedding (LLE) algorithm was developed to extract features directly from the hyperspectral scattering image data. Partial least squares discriminant analysis (PLSDA) and support vector machine (SVM) were applied to develop classification models based on the LLE, mean-LLE and mean spectra algorithms. The model based on the LLE algorithm achieved an overall classification accuracy of 80.4%, compared with 76.2% by the mean-LLE algorithm and 73.0% by the mean spectra method for two-class classification (i.e., mealy and nonmealy) coupled with PLSDA. For the SVM models, the LLE algorithm had an overall classification accuracy of 82.5%, compared with 79.4% by the mean-LLE algorithm and 78.3% by the mean spectra method. Hence, the LLE algorithm provided an effective means to extract hyperspectral scattering features for mealiness classification.