Open-environment machine learning

作     者：Zhi-Hua Zhou Zhi-Hua Zhou

作者机构：National Key Laboratory for Novel Software TechnologyNanjing University 

出 版 物：《National Science Review》 (国家科学评论(英文版))

年 卷 期：2022年第9卷第8期

页      面：211-221页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (61921006) the Collaborative Innovation Center of Novel Software Technology and Industrialization 

主　　题：machine learning artificial intelligence open-environment machine learning open ML 

摘      要：Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning in this article, are present to the community.Evidently, it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research,focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions and varied learning objectives, and discusses some theoretical issues.