Combining Learning and Optimisation
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: closed (10 December 2015) | Viewed by 9861
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine Learning and Optimisation are workhorses for computational intelligence techniques and data science. In fact, optimisation is key in many machine learning and data mining algorithms; at the same time optimisation methods that incorporate some form of learning strategy have an added level of sophistication, and consequently an increased ability to explore large search spaces efficiently. Finding new ways to combine learning with optimisation has tremendous potential towards providing powerful new methods, capable of solving larger and more complex problems than it was possible to do previously.
This Special Issue aims at publishing original research on new synergies between optimisation and machine learning. Both theoretical analyses and real world applications are encouraged.
Examples of topics include:
- Model building optimisation algorithms, estimation of distribution algorithms
- Learning surrogate functions for optimisation
- Efficient optimisation algorithms for solving complex machine learning tasks
- Dimensionality reduction for large scale learning and optimisation
- Compressive representations and randomisation for large scale problems
- Theoretical results and performance analyses of hybridised methods
- New hybrid algorithms
- Practical systems and real world applications of hybrid optimisation and machine learning methods
Dr Ata Kaban
Guest Editor
Manuscript Submission Information
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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. Computers is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- machine learning
- data mining
- optimisation
- global optimisation heuristics
- large scale problems
- high dimensional problems
- hybrid algorithms
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