Although the structural regularized support vector machine (SRSVM) can enhance the generalization capability of the standard support vector machine (SVM), its current version is used only for binary classification. To make SRSVM adapt to the K-class classification, the most direct approach is combining it with partitioning strategies, which may however lead to the following shortcomings: (1) Extracting structural information repeatedly for individual classifiers based on different class partitions increases the computational complexity. (2) Individual classifiers can hardly utilize complete data structural information. Under the basic framework of regular simplex support vector machine (RSSVM), we developed a novel structural improved regular simplex support vector machine (SIRSSVM). SIRSSVM generates only a single primal optimization problem, into which the data structural information within all classes is embedded, rather than using only partial structural information to construct individual classifiers as partitioning strategies do. Additionally, we modified the sequential minimization optimization (SMO)-type solver for RSSVM to adapt the proposed SIRSSVM model. Experimental results verified that our SIRSSVM could achieve excellent performance on both generalization capability and training efficiency.

尽管结构化正则化支持向量机（SRSVM）可以增强标准支持向量机（SVM）的泛化能力，但其当前版本仅用于二进制分类。使SRSVM适应K类分类，最直接的方法是将其与分区策略相结合，但这可能导致以下缺点：（1）根据不同的类分区重复提取各个分类器的结构信息会增加计算复杂性。（2）各个分类器几乎无法利用完整的数据结构信息。在常规单纯形支持向量机（RSSVM）的基本框架下，我们开发了一种新颖的结构改进型常规单纯形支持向量机（SIRSSVM）。SIRSSVM仅生成单个原始优化问题，将所有类中的数据结构信息嵌入其中，而不是像分区策略那样仅使用部分结构信息来构造单个分类器。另外，我们针对RSSVM修改了顺序最小化优化（SMO）型求解器，以适应提出的SIRSSVM模型。实验结果证明，我们的SIRSSVM可以在泛化能力和训练效率上均具有出色的表现。