Least Squares Maximum and Weighted Generalization-Memorization Machines
Shuai Wang Zhen Wang Yuan-Hai Shao
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.