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.

Firstly, we test the memorization ability and its influence of our HGMM on several small size datasets. The memory influence functions (i.e., formations (12), (13), (14) and (15)) were preloaded in our HGMM and evaluated by the m-fold cross validation (i.e., level-one-out validation, LOO for short). We set the baseline by setting the memory influence function be an identity matrix which is actually L2 loss SVM with decision (7) according to Theorem 4.3 (ii). Table II reports their highest LOO training and testing accuracies. From Table II, it is observed that our HGMM with either memory influence function has 100% training accuracies on all of these datasets.