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Training analysis of optimization models in machine learning | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 1453-1461 اصل مقاله (264.7 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5261 | ||
| نویسندگان | ||
| Ahmed Alridha* ؛ Fadhil Abdalhasan Wahbi؛ Mazin Kareem Kadhim | ||
| Department of Mathematics Science, Ministry of Education, Babylon, Iraq | ||
| تاریخ دریافت: 16 اسفند 1399، تاریخ بازنگری: 29 اردیبهشت 1400، تاریخ پذیرش: 22 خرداد 1400 | ||
| چکیده | ||
| Machine learning is fast evolving, with numerous theoretical advances and applications in a variety of domains. In reality, most machine learning algorithms are based on optimization issues. This interaction is also explored in the special topic on machine learning and large-scale optimization. Furthermore, machine learning optimization issues have several unique characteristics that are rarely seen in other optimization contexts. Aside from that, the notions of classical optimization vs machine learning will be discussed. Finally, this study will give an outline of these particular aspects of machine learning optimization. | ||
| کلیدواژه‌ها | ||
| machine learning؛ mathematical programming؛ optimization techniques | ||
| مراجع | ||
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