面向开放环境的机器学习理论研究进展

doi:10.16451/j.cnki.issn1003-6059.202312001

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Abstract In an open environment, machine learning is faced with various challenges, including varying category sets, non-identically distributed data and noise interference. These challenges can result in a significant decline in the performance of traditional machine learning systems built under the closed-world assumption. Therefore, open-environment machine learning is a research focus on artificial intelligence. In this paper, the current status and recent important advances in the theoretical study of open-environment machine learning are discussed from the perspectives of generalization, optimization, robustness and performance measurement. For generalization theory, the advances on the generalization performance analysis of open-set detection, transfer/meta learning and sparse learning approaches are introduced. For optimization theory, the advances on the theoretical analysis of random and sparse optimization, online and continual optimization, as well as distributed and federated optimization approaches are introduced. For robustness theory, the advances on robust learning under adversarial samples, random noise and noisy labels are introduced. For performance measurement, a number of widely used performance measurement criterions for open-environment machine learning are introduced. Finally, some prospects on the theoretical research trends of open-environment machine learning are provided.

Key words： Machine Learning Open Environment Generalization Theory Optimization Theory Robustness Theory Performance Measurement

Received: 09 October 2023

ZTFLH:

TP 391

Fund:Supported by National Key Research and Development Program of China(No.2018AAA0100400); National Natural Science Foundation of China(No.U21B2049); National Natural Science Foundation of China(No.61936005); National Natural Science Foundation of China(No.62222609); National Natural Science Foundation of China(No.62076236)

Corresponding Authors: YUAN Xiaotong, Ph.D., professor. His research interests include machine learning, stochastic optimization and computer vision.

About author:: ZHANG Xuyao, Ph.D., professor. His research interests include pattern recognition, machine learning and deep learning.
LIU Xi, master student. Her research inte-rests include federated learning, transfer learning and pattern recognition.
CHENG Zhen, Ph.D. candidate. His research interests include pattern recognition, machine learning and adversarial robustness.
LIU Chenglin, Ph.D., professor. His research interests include pattern recognition, machine learning, and document analysis and recognition.

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Cite this article:

YUAN Xiaotong,ZHANG Xuyao,LIU Xi等. Research Advances on Theory of Open-Environment Machine Learning[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(12): 1059-1071.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202312001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I12/1059

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