分类是指识别出样本所属的类别。识别前是否需要进行训练，可分为有监督分类和无监督分类。有监督分类(supervised classification):根据已知训练区提供的样本，通过计算选择特征参数，创建判别函数以对样本进行的分类。无监督分类(unsupervised classification):指人们事先对分类过程不施加任何的先验知识，而仅凭数据，即自然聚类的特性，进行“盲目”的分类；其分类的结果只是对不同类别达到了区分，但并不能确定类别的属性。

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "non-spam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.). Classification is an example of pattern recognition.