说明：  线性分类器可以实现线性可分的类别之间的分类决策，其形式简单，分类决策快速。但在许多模式识别的实际问题中，两个类的样本之间并没有明确的分类决策边界，线性分类器（包括广义线性分类器）无法完成分类任务，此时需要采用其它有效的分类方法。贝叶斯分类器就是另一种非常常见和实用的统计模式识别方法。
(Linear classifier can be realized linearly separable classification decision between classes in the form of simple, rapid classification decision. However, in many practical problems in pattern recognition and classification decisions there is no clear boundary, linear classifiers (including generalized linear classifier) was unable to complete the task of classification between two classes of samples, then the need to use other effective classification. Bayes classifier is another very common and useful statistical pattern recognition methods.)