This paper introduces the idea of learning uniformly ergodic Markov chain for one-against-all support vector machine (OAA-SVM) algorithm. We first obtain the generalization error of OAA-SVM with fast learning rate for uniformly ergodic Markov samples. We also propose a new OAA-SVM method with Markov sampling (OAA-SVM-MS). The experimental researches for benchmark repository confirm that the OAA-SVM-MS algorithm has significantly better performance in sampling and training total time, classification accuracy and the obtained classifier’s sparsity compared to the classical OAA-SVM algorithm and other multi-class SVM algorithms.

中文翻译：

---

OAA-SVM-MS：一种快速高效的多类分类算法

本文介绍了一种针对所有支持向量机（OAA-SVM）算法的统一遍历马尔可夫链的思想。对于均匀遍历的马尔可夫样本，我们首先获得具有快速学习速率的OAA-SVM的泛化误差。我们还提出了一种新的带马尔可夫采样的OAA-SVM方法（OAA-SVM-MS）。基准存储库的实验研究证实，与传统的OAA-SVM算法和其他多类SVM算法相比，OAA-SVM-MS算法在采样和训练总时间，分类准确性以及获得的分类器的稀疏性方面具有明显更好的性能。