表示到目前为止，训练级联分类器共用时27分2秒。
Training until now has taken 0 days 0 hours27 minutes 2 seconds.
级联分类器由17个弱分类器串联组成，其中每个弱分类器关注一个特征。
The cascade consists of 17 weak classifiers, each concentrating on one feature.
实验表明，本文树形结构的车辆识别方法在识别率和识别速度上优于级联分类器，具有较好的实时性和一定的鲁棒性。
Experimental results show that the tree structure classifier is better than cascade classifier in both detection accuracy and computational efficiency.
由于文本连通分量和非文本连通分量在特征上存在差异，大多数非文本会被级联分类器丢弃，而SVM则能在此结果上做进一步的验证，因此最终输出只有文本的二值图像。
Most of non-text CCs are filtered out by cascade classifier and the remaining CCs are further verified by SVM. The final outputs are binary images containing texts only.
为了实现目标的快速检测，提出了一种新的基于拉格朗日支持向量机(L -SVM)的线性级联式分类器的构造方法。
To detect objects quickly, a new method is presented to construct a cascade of linear classifiers with L-SVM (Lagrangian Support Vector Machine, L-SVM).
在设计时，采用两级SVM分类器级联的方式，核函数采用径向基函数，并且用网格搜索法来选取合适的参数。
Two-level-classifier is designed to recognized urinary sediments. The kernel function uses the radial basis function and grid-search method is used to select the parameters of SVM.
在设计时，采用两级SVM分类器级联的方式，核函数采用径向基函数，并且用网格搜索法来选取合适的参数。
Two-level-classifier is designed to recognized urinary sediments. The kernel function uses the radial basis function and grid-search method is used to select the parameters of SVM.
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