支持向量机作为一种经典的分类方法被广泛应用于恒星光谱分类领域。 该方法在实际应用中取得了较为理想的分类效果, 但其面临无法解决多分类问题的挑战。 在支持向量机的基础上, 提出多类支持向量机, 建立基于多类支持向量机的恒星光谱分类模型。 该方法的最大优势是经过一次分类过程, 可以确定多类样本的类属。 SDSS DR8恒星光谱数据上的比较实验表明, 本研究所提的方法较之已有多分类方法在分类性能上有一定的提升。

Support vector machine (SVM), a typical classification method, has been widely used in stellar spectra classification. It performs well in practice, while it encounters the multi-class classification challenge. In order to solve the problem above, multi-class support vector machine (MCSVM) was proposed in this paper based on the in-depth analysis of SVM. Meanwhile, the stellar spectra classification model based on multi-class support vector machine was constructed. The advantage of the proposed method is that the samples’ class can be determined by a classification process. Comparative experiments with the existed multi-class classification method on the SDSS DR8 datasets verify the effectiveness of the proposed method.