随着信息技术的迅猛发展，尤其是互联网行业的广泛应用，越来越多的领域出现了对海量、高速到达的数据实时处理需求。如何从浩瀚的“数据海洋”中挖掘有用的知识变得尤为重要。传统批处理模式的机器学习算法在面临大数据时变得力不从心，而在线学习通过流式计算框架，在内存中直接对数据实时运算，为大数据的学习提供了有力的工具，这类在线学习框架有望应对大数据背景下机器学习任务面临的困境与挑战。本文总结了经典和目前主流的在线学习算法，主要包括：（1）在线线性学习算法；（2）基于核的在线学习算法；（3）其他经典的在线学习算法；（4）在线学习算法的优化理论。本文介绍在线学习与深度学习结合方法的研究现状，探讨在线学习算法研究中的关键问题与应用场景，最后展望了在线学习下一步的研究方向。%With the development of information technology ,especially the wide application of Internet‐in‐volved products ,a large number of areas require real‐time processing of massive and high velocity data . How to learn informative knowledge from ″data ocean″ becomes increasingly important . Traditional batched machine learning algorithms come to be pale when dealing with big data .However ,the online learning framework employs streaming computing mode and deals with the data directly in the memory , which provides a promising tool for the learning of big data .This online learning framework has a bright prospect in facing difficulties and challenges when learning big data .This paper concludes the traditional and state‐of‐the‐art online learning algorithms ,the main contents include :(1) online linear learning algo‐rithms;(2) online kernel learning algorithms ;(3) other classical online learning algorithms ;(4) optimi‐zation methods of online learning algorithms .Additionally ,the implementation of online framework on deep learning models is then introduced to inspire interested researchers .Eventually ,this paper discusses the key issues and some applications of online learning algorithms ,which is followed by the research di‐rections of the research direction .
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