Multiple birth support vector machine (MBSVM) is a new classification algorithm, which includes the advantages of low complexity and high computing efficiency. However, the traditional MBSVM does not take into account the correlation sequence information among all dimensions of the samples when using the method to classify datasets, which limits the further improvement of the classification accuracy. Although some scholars have combined neural networks with support vector machine (SVM), these methods do not take into account the sequence correlation among different features. For the above problems, we present several variants of MBSVM algorithms to illustrate the validity and reliability of the theory: Multiple Birth Support Vector Machine based on Multilayer Perceptron (MLP-MBSVM), Multiple Birth Support Vector Machine based on Long-Short Term Memory Networks (LSTM-MBSVM), Multiple Birth Support Vector Machine based on Multilayer Perceptron and Long-Short Term Memory Networks(MLP-LSTM-MBSVM). After introducing multilayer perceptron and long-short term memory networks, these algorithms can take full account of the sequence correlation information between different features of samples. The experiments results show that the algorithms proposed in this paper are effective, and they can greatly improve the classification accuracy of multiple birth support vector machine.

多生支持向量机（MBSVM）是一种新的分类算法，具有低复杂度和高计算效率的优点。然而，传统的MBSVM在使用该方法对数据集进行分类时并未考虑样本所有维度之间的相关序列信息，这限制了分类精度的进一步提高。尽管一些学者将神经网络与支持向量机（SVM）相结合，但是这些方法并未考虑到不同特征之间的序列相关性。针对上述问题，我们提出了MBSVM算法的几种变体，以说明该理论的有效性和可靠性：基于多层感知器的多出生支持向量机（MLP-MBSVM），基于长期记忆网络（LSTM-MBSVM）的多重生育支持向量机，基于多层感知器和长期记忆网络（MLP-LSTM-MBSVM）的多重生育支持向量机。在引入多层感知器和长期短期记忆网络之后，这些算法可以充分考虑样本不同特征之间的序列相关信息。实验结果表明，本文提出的算法是有效的，可以大大提高多出生支持向量机的分类精度。这些算法可以充分考虑样本不同特征之间的序列相关信息。实验结果表明，本文提出的算法是有效的，可以大大提高多胎支持向量机的分类精度。这些算法可以充分考虑样本不同特征之间的序列相关信息。实验结果表明，本文提出的算法是有效的，可以大大提高多胎支持向量机的分类精度。