Handling noisy labels in classification is a core topic given the number of images available online with unprecise labels or even inaccurate ones. In our context, the label uncertainty is obtained by a fully gaze-based labelling process, called GBIE. We apply a noisy-label tolerant algorithm, P-SVM, which combines classification and regression processes. We have determined, among different strategies, a criterion of reliability to discriminate the most reliable labels involved in the classification from the most uncertain ones involved in the regression. The classification accuracy of the P-SVM is evaluated in different learning contexts, and can even compete in some cases with the baseline, i.e. a standard classification SVM trained with the true-class labels.
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