mlsanityData to 14 Oct 2026

Least Squares Maximum and Weighted Generalization-Memorization Machines

arXiv 2308.16456 ↗First posted 31 Aug 2023stat.ML cs.LGcited by 0

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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.

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.

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References · 13

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2017A Closer Look at Memorization in Deep Networkscited by 942
  3. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 927
  4. 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
  5. 2020Rethinking Bias-Variance Trade-off for Generalization of Neural Networkscited by 107
  6. 2018Deep Reinforcement Learning of Marked Temporal Point Processescited by 33
  7. 2020Cost-Sensitive Portfolio Selection via Deep Reinforcement Learningcited by 21
  8. 2018DNN or k-NN: That is the Generalize vs. Memorize Questioncited by 16
  9. 2023ResMem: Learn what you can and memorize the restcited by 13
  10. 2016Unbounded Human Learning: Optimal Scheduling for Spaced Repetitioncited by 12
  11. 2021Improving Artificial Teachers by Considering How People Learn and Forgetcited by 4
  12. 2022Generalization-Memorization Machinescited by 2
1 more
  1. 2017Sequential Multi-Class Labeling in Crowdsourcingcited by 1

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