Pairwise classification depends on two input examples instead of one example and predicts whether the two input examples belong to the same class or to different classes. In this paper we investigate online pairwise support vector machine (OPSVM) algorithm with independently and identically distributed (i.i.d.) and non-i.i.d. samples. We first establish the convergence rates of the last iteration for OPSVM algorithm and obtain the fast convergence rates of OPSVM algorithm with strongly mixing sequence or i.i.d. samples for the case of polynomially decaying step size. We also introduce a novel OPSVM algorithm based on Markov selective sampling (OPSVM-MSS). The experimental results based on benchmark repository display that the classifier induced by OPSVM-MSS algorithm not only has smaller misclassification rate, but also the sampling and training total time is shorter compared to the classifier induced by the classical OPSVM algorithm based on randomly independent sampling (OPSVM-RIS).

成对分类依赖于两个输入示例而不是一个示例，并预测两个输入示例是属于同一类还是属于不同类。在本文中，我们研究具有独立且均匀分布的（iid）和非iid样本的在线成对支持向量机（OPSVM）算法。对于多项式递减步长的情况，我们首先确定OPSVM算法最后一次迭代的收敛速度，并获得具有强烈混合序列或iid样本的OPSVM算法的快速收敛速度。我们还介绍了一种基于马尔可夫选择性抽样（OPSVM-MSS）的新颖OPSVM算法。基于基准存储库的实验结果表明，OPSVM-MSS算法引入的分类器不仅具有较小的误分类率，