In this paper, we propose a new way of remembering by introducing a memory
influence mechanism for the least squares support vector machine (LSSVM).
Without changing the equation constraints of the original LSSVM, this
mechanism, allows an accurate partitioning of the training set without
overfitting. The maximum memory impact model (MIMM) and the weighted impact
memory model (WIMM) are then proposed. It is demonstrated that these models can
be degraded to the LSSVM. Furthermore, we propose some different memory impact
functions for the MIMM and WIMM. The experimental results show that that our
MIMM and WIMM have better generalization performance compared to the LSSVM and
significant advantage in time cost compared to other memory models.

本文提出了一种新的记忆影响机制，通过引入最小二乘支持向量机（LSSVM）的记忆机制，在不改变原始 LSSVM 的等式约束的情况下，实现了对训练集的精确划分而无过拟合现象。提出了最大记忆影响模型（MIMM）和加权影响记忆模型（WIMM），证明这些模型可以退化为 LSSVM。此外，我们还为 MIMM 和 WIMM 提出了一些不同的记忆影响函数。实验结果表明，我们的 MIMM 和 WIMM 在泛化性能上优于 LSSVM，并在时间成本上比其他记忆模型具有显著优势。