可扩展机器学习中残缺数据补全方法研究

Research on Incomplete Data Completion Method in Extensible Machine Learning

现代社会中的各行各业都在广泛运用互联网的相关技术,最具代表性的便是网络大数据的搜集、处理、储存以及输出反馈工作。现对可扩展机器学习中的残缺数据进行补全方法的相关研究。通过对残缺数据补全方法的设计,可以发现数据残缺问题的出现主要是由数据属性残缺所导致的。通过对数据属性相关算法的设计,可以有效完成残缺数据的补全工作。通过对残缺数据补全方法的实验论证,可以发现G-G估算法对残缺数据的补全有重大实践意义。

The Internet is widely used in all walks of life in modern society.The most representative is the collection,processing,storage and output feedback of large network data.The related research on incomplete data completion in extensible machine learning is done.Through the design of incomplete data complement method,we can find that the problem of data incomplete is mainly caused by the incomplete data attribute.Through the design of the data attribute correlation algorithm,the incomplete data can be completed effectively.Through the experimental demonstration of the incomplete data complement method,the G-G estimation can be found.The completion of incomplete data is of great practical significance.