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该算法在第一次小样本训练时引入了遗忘因子,该因子使支持向量数减少了28%。
The algorithm introduced the forgetting factor to get the support vectors at the first training. The number of support vectors is decreased by 28%.
几何特征的加入使得小样本训练的粗分类器的应用成为现实,提高了眼睛检测的精度。
Then with the characteristics of symmetry of the eyes some of the geometric characteristics are adopted for correction .
该算法利用预测误差阈值进行样本的取舍,在尽量保留有用信息的情况下减小样本训练规模。
This algorithm USES the prediction error threshold to retain the useful information to decrease sample training scale.
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