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28 February 2021
Advanced Machine Learning and Optimization Theories and Algorithms for Heterogeneous Data Analytics
Machine learning has been successfully applied to many data analytical tasks, which usually formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems. Accordingly, optimization becomes a crucial tool and plays a key role in machine learning and multimedia data analysis tasks. On the other hand, machine learning and the applications in heterogeneous data analytics are not simply the consumers of optimization technology but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions. The objective of this special issue is to collect and report on recent high quality research that shows the most recently advanced methods in optimization and machine learning for heterogeneous data computing. This special issue is a following-up of the 2020 edition of the 16th International Conference on Computational Intelligence and Security.
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Please select the Special Issue: "Advanced Machine Learning and Optimization Theories and Algorithms for Heterogeneous Data Analytics ", when submitting your paper to ScholarOne. In this special issue, we mainly accept the significantly extended papers whose original versions have been accepted by the 16th International Conference on Computational Intelligence and Security (CIS'20), although the papers from the public call-for-papers will be considered as well.
All submissions will be reviewed by experts in the field based on originality, significance, quality and clarity. The length of a submitted paper should not more than 10 pages in the IEEE two-column format.
The expected publication date is on October 31, 2021.
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