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- W2909324897 endingPage "828" @default.
- W2909324897 startingPage "807" @default.
- W2909324897 abstract "This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted." @default.
- W2909324897 created "2019-01-25" @default.
- W2909324897 creator A5080364245 @default.
- W2909324897 creator A5084563518 @default.
- W2909324897 creator A5091624106 @default.
- W2909324897 date "2021-05-01" @default.
- W2909324897 modified "2023-10-17" @default.
- W2909324897 title "Optimization problems for machine learning: A survey" @default.
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