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机器学习中的优化:最新进展和当前挑战

Optimization in Machine Learning: Recent Developments and Current Challenges
课程网址: http://videolectures.net/opt08_wright_oimlr/  
主讲教师: Stephen J. Wright
开课单位: 威斯康星大学
开课时间: 2008-12-20
课程语种: 英语
中文简介:
近年来,使用优化作为制定机器学习问题的框架已变得更加普遍。在某些情况下,机器学习问题的需求超出了传统优化范例的范围。虽然现有的优化配方和算法是解决方案策略的良好起点,但必须在优化和机器学习的界面上进行重要的工作,以设计利用应用程序的特殊功能并在非常大的数据上运行良好的策略。集。本演讲从优化角度回顾了最近的发展,重点关注过去三年的活动,特别关注机器学习应用在优化领域推动新算法或分析的问题。我们还讨论了当前的一些挑战,重点介绍了一些最新的优化发展,这些发展可能对机器学习应用有用。
课程简介: The use of optimization as a framework for formulating machine learning problems has become much more widespread in recent years. In some cases, the demands of the machine learning problems go beyond the scope of traditional optimization paradigms. While existing optimization formulations and algorithms serve as an good starting point for the solution strategies, important work must be carried out at the interface of optimization and machine learning to devise strategies that exploit the special features of the application and that perform well on very large data sets. This talk reviews recent developments from an optimization perspective, focusing on activity during the past three years, and looking in particular at problems where the machine learning application has motivated novel algorithms or analysis in the optimization domain. We also discuss some current challenges, highlighting several recent developments in optimization that may be useful in machine learning applications.
关 键 词: 机器学习; 优化配方; 新算法
课程来源: 视频讲座网
最后编审: 2020-07-14:yumf
阅读次数: 79