Optimization Method and Its Applications in Machine Learning
A special issue of Axioms (ISSN 2075-1680).
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 421
Special Issue Editors
Interests: optimization theory and methods; non-smooth optimization; statistical analysis; nonlinear equations; compression sensing; image processing; optimization methods in engineering
Special Issues, Collections and Topics in MDPI journals
Interests: optimization; machine learning; biology computation
Interests: optimization theory and methods; numerical linear algebra; statistical analysis
Special Issue Information
Dear Colleagues,
This Special Issue on the "Optimization Method and Its Applications in Machine Learning" focuses on exploring the use of machine learning and optimization techniques, and how these methods are applied in engineering mathematics. It aims to investigate their synergistic relationship and demonstrate their potential in solving complex engineering problems. The scope of this Special Issue includes topics such as the application of machine learning algorithms, integration of optimization methods with machine learning, development of hybrid models, utilization of machine learning for parameter estimation and system identification, optimization-based approaches to enhance machine learning, and case studies demonstrating real-world applications.
This Special Issue supplements the existing literature by providing comprehensive coverage of the current state-of-the-art research in this area. It integrates diverse perspectives from various disciplines, including engineering, mathematics, computer science, and optimization. This Special Issue presents novel methodologies and applications that combine machine learning and optimization, offers comparative analyses and evaluations of different techniques, and includes case studies and practical implementations. It also discusses future research directions and challenges in the field, guiding researchers and inspiring further exploration.
Prof. Dr. Gonglin Yuan
Prof. Dr. Shengwei Yao
Dr. Xiaoliang Dong
Prof. Dr. Stefania Bellavia
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- optimization algorithms
- engineering mathematics
- convergence
- application
- interdisciplinary collaboration
