Support vector machine (SVM) and neural networks (NN) have strong
complementarity. SVM focuses on the inner operation among samples while NN
focuses on the operation among the features within samples. Thus, it is
promising and attractive to combine SVM and NN, as it may provide a more
powerful function than SVM or NN alone. However, current work on combining them
lacks true integration. To address this, we propose a sample attention memory
network (SAMN) that effectively combines SVM and NN by incorporating sample
attention module, class prototypes, and memory block to NN. SVM can be viewed
as a sample attention machine. It allows us to add a sample attention module to
NN to implement the main function of SVM. Class prototypes are representatives
of all classes, which can be viewed as alternatives to support vectors. The
memory block is used for the storage and update of class prototypes. Class
prototypes and memory block effectively reduce the computational cost of sample
attention and make SAMN suitable for multi-classification tasks. Extensive
experiments show that SAMN achieves better classification performance than
single SVM or single NN with similar parameter sizes, as well as the previous
best model for combining SVM and NN. The sample attention mechanism is a
flexible module that can be easily deepened and incorporated into neural
networks that require it.

支持向量机（SVM）和神经网络（NN）具有很强的互补性。为了解决将它们真正整合起来的问题，本研究提出了一种样本关注记忆网络（SAMN），通过将样本关注模块、类原型和记忆块纳入神经网络中，有效地结合了 SVM 和 NN。SAMN 在多分类任务中显著提高了分类性能，而且在参数规模相似的情况下超过了单独的 SVM 或 NN 模型，以及之前最佳的 SVM 和 NN 结合模型。样本关注机制是一个灵活的模块，可以轻松深化并应用于需要的神经网络中。