基于复合核支持向量回归机的多类分类算法

针对传统支持向量机(SVM)在解决多类分类问题时需要训练多个分类器、存在不可分区域等问题，研究了基于支持向量回归机的多类分类算法。利用回归思想求解多类分类问题，将分类样本作为回归输入，样本的类别标识作为回归输出，通过支持向量回归机训练拟合出各样本与其类别标识之间的函数关系。将待分类样本代入回归函数，对其输出取整后即可得到样本类别。该算法仅使用1个分类器，明显简化了分类过程。另外，引入复合核函数来提高支持向量回归机的性能。采用加州大学欧文分校(UCI)例题库中的多类分类问题进行仿真验证，并将改进算法与传统算法作对比，结果表明改进算法在分类速度和准确率上都有显著提高。

Multi-class classification method based on support vector regression machine with composite kernel function

Aiming at the problem that there is inseparable region and more than one traditional Support Vector Machine(SVM) classifiers need to be trained in the multi-class classification problem, the support vector regression machine based multi-class classification method is researched. This method utilizes regression theory to solve multi-class classification problems, in which the classification samples are served as regression input, and their class labels are served as regression output, then the relationship between samples and their class labels are fitted by support vector regression machine method. The samples are classified into the regression function, and the class labels are obtained by adding a rounding operation to the regression output. This method uses only one classifier, which significantly simplifies the classification process. In addition, the composite kernel function is introduced to improve the performance of the support vector regression machine. The datasets of multi-class classification problems selected from University of California Irvine(UCI) database are used for simulation. Compared with traditional multi-class support vector machine, both classification speed and accuracy of the proposed method have been significantly improved.