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.
machine learningartificial intelligenceopen-environment machine learningopen ML
Zhi-Hua Zhou
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National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China
国家自然科学基金Collaborative Innovation Center of Novel Software Technology and Industrialization
NETL
NSTL
万方数据
维普
