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DATA TYPE RECOGNITION, MODEL TRAINING AND RISK RECOGNITION METHODS, APPARATUSES AND DEVICES

机译:数据类型识别,模型训练和风险识别方法,装置和设备

摘要

Data type recognition and model training methods and apparatuses, and computer devices are provided. The model training method includes acquiring a first sample data set, and using the first sample data set to train an anomaly detection model; and detecting an abnormal sample data set from a second sample data set by means of the anomaly detection model, and using the abnormal sample data set to train a classification model. By using this method, an amount of scoring events of the classification model can be reduced, and relatively balanced sample data sets can also be provided for training, to obtain the classification model with a higher accuracy.
机译:提供了数据类型识别和模型训练方法和装置以及计算机设备。该模型训练方法包括:获取第一样本数据集,并利用该第一样本数据集训练异常检测模型;通过所述异常检测模型从第二样本数据集中检测出异常样本数据集,并利用所述异常样本数据集训练分类模型。通过使用这种方法,可以减少分类模型的评分事件的数量,并且还可以提供相对平衡的样本数据集进行训练,从而获得具有更高准确性的分类模型。

著录项

  • 公开/公告号US2019303569A1

    专利类型

  • 公开/公告日2019-10-03

    原文格式PDF

  • 申请/专利权人 ALIBABA GROUP HOLDING LIMITED;

    申请/专利号US201916444156

  • 发明设计人 YU CHENG;

    申请日2019-06-18

  • 分类号G06F21/55;G06N20;

  • 国家 US

  • 入库时间 2022-08-21 12:08:31

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