Back to Search Start Over

Recent Advances in Large Margin Learning.

Authors :
Guo, Yiwen
Zhang, Changshui
Source :
IEEE Transactions on Pattern Analysis & Machine Intelligence. Oct2022, Vol. 44 Issue 10, p7167-7174. 8p.
Publication Year :
2022

Abstract

This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade. We generalize the formulation of classification margins from classical research to latest DNNs, summarize theoretical connections between the margin, network generalization, and robustness, and introduce recent efforts in enlarging the margins for DNNs comprehensively. Since the viewpoint of different methods is discrepant, we categorize them into groups for ease of comparison and discussion in the paper. Hopefully, our discussions and overview inspire new research work in the community that aim to improve the performance of DNNs, and we also point to directions where the large margin principle can be verified to provide theoretical evidence why certain regularizations for DNNs function well in practice. We managed to shorten the paper such that the crucial spirit of large margin learning and related methods are better emphasized. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01628828
Volume :
44
Issue :
10
Database :
Academic Search Index
Journal :
IEEE Transactions on Pattern Analysis & Machine Intelligence
Publication Type :
Academic Journal
Accession number :
159210566
Full Text :
https://doi.org/10.1109/TPAMI.2021.3091717