Mathematical Optimization in Machine Learning, Computer Vision, and Data Mining, 2nd Edition

Dear Colleagues,

This Special Issue welcomes scientific manuscripts on two relevant topics for mathematical optimization in machine learning, computer vision, pattern recognition, and data mining.

1. The adaptation of existing optimization techniques from mathematics or the development of novel approaches for addressing interesting problems in machine learning, computer vision, pattern recognition, and data mining. This includes improvements to existing machine learning, deep learning, computer vision pattern recognition, and data mining algorithms. Improvements can include, but are not limited to, faster convergence rates, improved bounds, improved efficiency or memory consumption, better performance, etc. Improvements should be significant and demonstrated by corresponding empirical and theoretical evaluation.
2. The design of novel machine learning, computer vision pattern recognition, or data mining approaches to solve a specific domain task (this includes improving solutions for existing problems using, for example, heterogeneous or big data). The approach should not be a simple adaptation of existing algorithms to novel data. Proposed improvements must be supported by appropriate experiments and rigorous explanations of why existing approaches are not applicable.

Dr. Valsamis Ntouskos
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• mathematical optimization
• machine learning
• computer vision
• image restoration
• image enhancement
• pattern recognition
• data mining
• convergence rate
• theoretical bounds
• time complexity
• execution time
• memory consumption
• algorithm performance
• domain-specific problems

• Mathematical Optimization in Pattern Recognition, Machine Learning and Data Mining in Mathematics (5 articles)