When building a binary classifier, many practitioners immediately jump to logistic regression because it's simple.
当构建一个二元分类器时，很多实践者会立即跳转到逻辑回归，因为它很简单。
A binary tree classifier based on multivariate step-wise regression was designed and implemented.
作者设计并实现了一个基于多变元逐步回归的二叉树分类器。
A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
In the designing of classifier module, we convert a 4 classes classifying problem into 3 binary classify problems by using a hierarchy layer classify architecture.
在分类器的设计上，本架构使用了三层二分的分类器设计思想，将一个困难的4分问题转化为3个层次上的二分问题。
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.
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.
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。
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