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.
基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。
The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.
不幸的是，他们的小样本阻止作者认为这样的训练计划有预防的作用。
Unfortunately, their small sample size precluded the authors from suggesting the training program had a preventive effect.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
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