数据采集与处理2016，Vol.31Issue(6)：1067-1082,16.
在线学习算法综述
Survey on Online Learning Algorithms
摘要
Abstract
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 .关键词
在线学习/核/优化理论/概念漂移/深度学习Key words
online learning/kernel/optimization theory/concept drafting/deep learning分类
信息技术与安全科学引用本文复制引用
潘志松,唐斯琪,邱俊洋,胡谷雨..在线学习算法综述[J].数据采集与处理,2016,31(6):1067-1082,16.基金项目
国家自然科学基金（61473149）资助项目。 （）
