Optimization Models for Machine Learning: A Survey
Abstrak
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.
Topik & Kata Kunci
Penulis (3)
Claudio Gambella
Bissan Ghaddar
Joe Naoum-Sawaya
Akses Cepat
- Tahun Terbit
- 2019
- Bahasa
- en
- Total Sitasi
- 237×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1016/J.EJOR.2020.08.045
- Akses
- Open Access ✓