基于特权信息的SVM模型研究及应用 Research and Application of SVM Model Based on Privileged Information

当使用支持向量机(Support Vector Machines, SVM)进行分类时，可能会遇到训练样本中存在额外信息的情况。它是不可忽视的，因为它会严重影响测试样本的分类准确率。本文提出了一种基于特权信息的SVM模型，其包含了特权信息分布的多种情况，能有效地解决多种特权信息分布问题。本文首先叙述了SVM分类的基本原理，在简单介绍特权信息概念后，提出了基于特权信息的SVM模型及其特例。接着介绍了基于特权信息的SVM模型应用，最后对该研究领域存在的问题及发展方向进行了总结。 When using Support Vector Machine (SVM) to training classification model, we may encounter ad-ditional information in training samples. Because it will seriously affect classification accuracy of test samples, it really can’t be ignored. In this paper, a SVM model based on privilege information is proposed, which contains many cases of privilege information and can solve many kinds of dis-tribution problems effectively. This paper first described the basic principles of SVM classification, and then it introduced the concept of privilege information. Next this paper proposed a SVM model based on privilege information and gave its special cases. Then, the application of SVM model based on privilege information was introduced. Finally, the existing problems and the development direction of this research were summarized.