URL: http://www.aimsciences.org/journal/A0000-0001
 
The journal Mathematical Foundations of Computing, indexed in ESCI, is calling for papers for a theme issue on kernel-based methods. Kernel-based methods have been used for various purposes in many fields including density estimation and regression in statistics, support vector machines and learning kernels in machine learning, and mesh-free schemes in numerical analysis and uncertainty quantification. They play an essential role in finding filters and features in the recent development of deep learning and deep neural networks. The aim of this theme issue is to report recent research results on kernel-based methods, carry out interactions among researchers, discuss important research problems and directions, and promote kernel methods in various research areas. Topics of the theme issue include but are not limited to Regularization schemes and deep learning 
Multivariate approximation 
Meshless methods Kernel-based methods in data science, image and signal processing 
Kernel-based function spaces and complexity 
Kernel-based algorithms and computing
The submission deadline is December 31, 2019. The submitted papers will be peer-reviewed following the reviewing procedures of the journal Mathematical Foundations of Computing. The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and anexplanation should be provided about how such papers have been extended to be considered for this special issue. The submitted papers should follow the submission format and guidelines found at the journal webpage:
https://www.aimsciences.org/journal/A0000-0001

For more details about the theme issue and the journal Mathematical Foundations of Computing in general, please contact Editor-in-Chief of the journal, Ding-Xuan Zhou (mazhou@cityu.edu.hk).

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