机器学习模型面向数据内容审核的研究
Research on Machine Learning Model for Data Content Audit
吴新野1
作者信息
- 1. 上海德拓信息技术股份有限公司,上海 200233
- 折叠
摘要
用户对互联网数据的需求量越来越大,需要审核的数据越来越多.然而,现实中存在大量虚假、非法、侵权等违规数据,对用户进行审核也变得越来越困难.机器学习算法可以通过训练数据进行预测和判定.当发现模型预测与实际情况不符时,可以及时采取有效措施,纠正模型错误.基于此,重点对机器学习模型面向数据内容审核进行分析和研究.
Abstract
Users'demand for Internet data is growing,and more and more data need to be audited.However,in reality,there is a large amount of false,illegal,and infringing data,making it increasingly difficult to audit users.Machine learning algorithms can make predictions and judgments through training data.When it is found that the model prediction does not match the actual situation,effective measures can be taken in a timely manner to correct model errors.Based on this,the focus is on analyzing and researching machine learning models for data content auditing.
关键词
机器/学习模型/数据/内容审核Key words
machine/learning model/data/content audit引用本文复制引用
出版年
2024
NETL
NSTL
万方数据