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%.

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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 .

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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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