# Least Squares Maximum and Weighted Generalization-Memorization Machines

arXiv: https://arxiv.org/abs/2308.16456 · HTML: https://mlsanity.com/paper/2308.16456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Rethinking Bias-Variance Trade-off for Generalization of Neural Networks](https://mlsanity.com/paper/2002.11328/) (2020)
- [Deep Reinforcement Learning of Marked Temporal Point Processes](https://mlsanity.com/paper/1805.09360/) (2018)
- [Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning](https://mlsanity.com/paper/2003.03051/) (2020)
- [DNN or k-NN: That is the Generalize vs. Memorize Question](https://mlsanity.com/paper/1805.06822/) (2018)
- [ResMem: Learn what you can and memorize the rest](https://mlsanity.com/paper/2302.01576/) (2023)
- [Unbounded Human Learning: Optimal Scheduling for Spaced Repetition](https://mlsanity.com/paper/1602.07032/) (2016)
- [Improving Artificial Teachers by Considering How People Learn and Forget](https://mlsanity.com/paper/2102.04174/) (2021)
- [Generalization-Memorization Machines](https://mlsanity.com/paper/2207.03976/) (2022)
- [Sequential Multi-Class Labeling in Crowdsourcing](https://mlsanity.com/paper/1711.02128/) (2017)

## Abstract (arXiv)

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.