Generalization-Memorization Machines
Not on the map. Why?
Abstract (arXiv)
Classifying the training data correctly without over-fitting is one of the goals in machine learning. In this paper, we propose a generalization-memorization mechanism, including a generalization-memorization decision and a memory modeling principle. Under this mechanism, error-based learning machines improve their memorization abilities of training data without over-fitting. Specifically, the generalization-memorization machines (GMM) are proposed by applying this mechanism. The optimization problems in GMM are quadratic programming problems and could be solved efficiently. It should be noted that the recently proposed generalization-memorization kernel and the corresponding support vector machines are the special cases of our GMM. Experimental results show the effectiveness of the proposed GMM both on memorization and generalization.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 8 Jul 2022.
Builds on
7 of 34 references.
References · 7
- 2017A Closer Look at Memorization in Deep Networkscited by 942
- 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 657
- 2019Does Learning Require Memorization? A Short Tale about a Long Tailcited by 460
- 2019Accurate Uncertainty Estimation and Decomposition in Ensemble Learningcited by 42
- 2020When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Datacited by 11
- 2019Robust subspace clustering by Cauchy loss functioncited by 5
- 2017Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problemscited by 2
Built on by
Citing papers per year: 2023: 2.
2 papers.