通过训练后，将20个未分类的人工序列样本和1 82个自然序列样本提取特征形成特征向量并输入两个网络进行分类。
After the training, characters are extracted from the2 0 unclassified artificial sequence samples and1 82 natural sequence samples to form the character vectors as input of the two NN for clustering.
利用它对签名样本的动态信息时间序列进行校正，可以提高签名特征向量在特征空间上分布的聚拢性，拉开真、伪签名特征向量在特征空间上的距离。
It can effectively increase the compactness of the feature vectors of genuine signatures, and therefore enlarge the distance from the feature vectors of forgery ones.
通过训练后，将20个未分类的人工序列样本和182个自然序列样本提取特征向量并输入两个网络进行分类。
After the training, characters are extracted from the 20 unclassified artificial sequence samples and 182 natural sequence samples to form the character vectors as input of the two NN for clustering.
通过训练后，将20个未分类的人工序列样本和182个自然序列样本提取特征向量并输入两个网络进行分类。
After the training, characters are extracted from the 20 unclassified artificial sequence samples and 182 natural sequence samples to form the character vectors as input of the two NN for clustering.
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