Related papers: Open Environment Machine Learning

Open Environment Machine Learning

URL: http://arxiv.org/abs/2206.00423v1
Date: Wed, 1 Jun 2022 11:57:56 GMT
Title: Open Environment Machine Learning
Authors: Zhi-Hua Zhou
Abstract summary: Conventional machine learning studies assume close world scenarios where important factors of the learning process hold invariant. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions, varied learning objectives, and discusses some theoretical issues.
Score: 84.90891046882213
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Conventional machine learning studies generally assume close world 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 world scenarios where important factors are subject to change, called open environment machine learning (Open ML) in this article, are present to the community. Evidently it is a grand challenge for machine learning turning from close world to open world. 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, varied learning objectives, and discusses some theoretical issues.

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