Least Squares Maximum and Weighted Generalization-Memorization Machines
This work addresses overfitting in LSSVM for machine learning applications, but it is incremental as it builds on existing LSSVM methods.
The paper tackles the problem of overfitting in least squares support vector machines (LSSVM) by introducing a memory influence mechanism, resulting in better generalization performance and significant time cost advantages compared to other models.
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.