随着社会的不断进步，对于数据处理的要求越来越高，人们需要处理的数据非常庞大，比如工业信息、环境信息，有些数据没有明确的标签供人们进行处理，当存在海量的无标签数据时，如何从海量的无标签数据中获得有用的数据，是需要重点研究的问题。半监督学习不仅能够更好地利用标签进行数据的处理，同时还可以通过无标签数据进行指导以提高分类的精度。因此，本文将重点研究基于支持向量机的半监督学习分类方法。
With the continuous progress of the society, the demand for data processing is getting higher and higher, the amount of data that people have to deal with, such as industrial information, environmental information, some data don't have clear labels for people to process, when there are massive unlabeled data, how to get useful data from the massive unlabeled data is a key problem to be studied. Semi-supervised learning can not only make better use of tags for data processing, but also improve the accuracy of classification through the guidance of unlabeled data. Therefore, this paper will focus on the semi-supervised learning classification method based on support vector machine.
