Optimization problems for machine learning: A survey

•Machine learning approaches are presented as optimization formulations.•Supervised and unsupervised learning approaches are surveyed.•Emerging applications in machine learning and deep learning are presented.•The strengths and the shortcomings of the optimization models are discussed.•Potential res...

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Veröffentlicht in:European journal of operational research Jg. 290; H. 3; S. 807 - 828
Hauptverfasser: Gambella, Claudio, Ghaddar, Bissan, Naoum-Sawaya, Joe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.05.2021
Schlagworte:
Analytics
Deep learning
Machine learning
Mathematical programming
Deep learning
Analytics
Machine learning
Mathematical programming
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•Machine learning approaches are presented as optimization formulations.•Supervised and unsupervised learning approaches are surveyed.•Emerging applications in machine learning and deep learning are presented.•The strengths and the shortcomings of the optimization models are discussed.•Potential research directions and open problems are highlighted. 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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2020.08.045