先验信息优化的S3VM算法模型研究
A Novel Algorithm Model Design of S3VM Improved by Prior Information

在没有充足的标记数据支撑的情况下，传统的半监督学习算法的运算精确性和效率会显著降低。因此，在大数据应用中，面对海量增长的数据和其中所包含的庞杂信息，需要对传统算法进行优化。但相对于数据特征，数据的先验信息是数据集中的一个未被重视的重要组成部分。文中建立了合适的数学模型引入先验信息；针对静态先验信息、动态先验信息2种不同形式先验信息，对传统S3VM算法进行了优化。测试实验验证了模型的效果。
If there is insufficient labeled data to support, the traditional semi-supervised learning algorithm has a significant reduction in computational accuracy and efficiency. Therefore, facing the massive growth of data and the included complex information, traditional algorithms must be improved for the applications of Big-date. The prior information of data, comparing with the data feature, is an important component of the data set that hasn't been focused on. This paper establishes an appropriate mathematical model to introduce the priori information, and designs two models to improve the traditional S3VM algorithm by two different kind of prior information, static priori information and dynamic priori information. The test experiment verifies the effect of the model