数据流的主动学习：综述
Active learning for data streams: a survey
AI总结：
本文综述了面向数据流的在线主动学习方法，系统梳理了实时选择最具信息量观测进行标注的各类技术，并分析了其优势、局限及面临的挑战与机遇。
摘要：
在线主动学习是机器学习中的一种范式，旨在从数据流中选择最具信息量的数据点进行标注。近年来，最小化收集已标注观测数据相关成本的问题受到了广泛关注，尤其是在数据仅以未标注形式可用的现实应用中。对每个观测进行标注可能耗时且成本高昂，使得获取大量已标注数据变得困难。为克服这一问题，过去几十年中提出了许多主动学习策略，旨在选择最具信息量的观测进行标注，以提升机器学习模型的性能。这些方法可大致分为两类：静态基于池的主动学习和基于流的主动学习。基于池的主动学习涉及从封闭的未标注数据池中选择一个观测子集，并已成为许多综述和文献回顾的焦点。然而，数据流的日益普及导致专注于在线主动学习的方法数量增加，该方法涉及在观测随流到达时持续选择和标注它们。本文旨在概述最近提出的用于实时从数据流中选择最具信息量观测的方法。我们回顾了已提出的各种技术，并讨论了它们的优势与局限，以及该研究领域中存在的挑战与机遇。
英文摘要：
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
