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.

翻译：本文提出一种新的记忆方式，通过为最小二乘支持向量机（LSSVM）引入记忆影响机制，在不改变原始LSSVM方程约束的条件下，该机制能够实现训练集的精确划分而避免过拟合。进而提出最大记忆影响模型（MIMM）和加权影响记忆模型（WIMM），并证明这些模型可退化为LSSVM。此外，我们针对MIMM和WIMM提出了若干不同的记忆影响函数。实验结果表明，与LSSVM相比，我们的MIMM和WIMM具有更优的泛化性能，并在时间成本上相较于其他记忆模型具有显著优势。