When a training sample set is labelled and unbalanced so that the class with small size will reach a much high error rate of classification, a weighted SVM algorithm, i. e., dual v-SVM, is introduced into anomaly detection. 针对有标定的不均衡数据集对于数目较少的那类样本分类错误率较高的情况,引入了加权SVM算法-双v-SVM算法来进行异常检测;
Conditional random is regarded as the initial classifier, marking classified against the few number of labelled samples, and then use self-training algorithm, to label the unlabeled samples in order to add them to the training set. 以条件随机场模型为初始分类器,通过自训练算法,对未标注样本进行标注选取置信度较高的数据加入到训练集。