SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。

When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.

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SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。

When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.

youdao