| 机器学习中的最优化模型与对偶 |
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| 引用本文： | 刘红英,杨茜.机器学习中的最优化模型与对偶[J].数学的实践与认识,2017(4):225-233. |
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| 作者姓名： | 刘红英 杨茜 |
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| 作者单位： | 北京航空航天大学数学与系统科学学院,北京,100083 |
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| 基金项目： | 国家自然科学基金(61172060 |
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| 摘 要： | 数学最优化是以数学的方式来刻画和找出问题最优解的一门学科.机器学习利用数据构造预测方法,并对这些方法进行研究.介绍了机器学习中与支持向量机和稀疏重构相关的最优化模型.在此基础上,给出了三个典型最优化模型的对偶问题,并详细地讨论了对偶在求解这些问题中的应用.
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| 关 键 词： | 对偶 机器学习 支持向量机 稀疏重构 |
Optimization Models and Dualities Study in Machine Learning |
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| LIU Hong-ying,YANG Qian.Optimization Models and Dualities Study in Machine Learning[J].Mathematics in Practice and Theory,2017(4):225-233. |
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| Authors: | LIU Hong-ying YANG Qian |
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| Abstract: | The mathematical optimization is a subject which focuses on characterizing and finding the optimal solution of the practical problem.Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.In this paper,the support vector machine and sparse reconstruction in machine learning field are introduced which are typical optimization models.The duality with application to these optimization problems is discussed. |
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| Keywords: | duality machine learning support vector machine sparse reconstruction |
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