About this Research Topic
For many decades, scientists strive to develop intelligent machines to reproduce or improve human intelligent actions. Machine Learning (ML), a research branch of artificial intelligence, aims to learn and simulate specific intelligent human actions and has been applied to a large variety of artificial and natural systems.
At first glance, it is tempting to automize solving complex problems by machines. Specifically, in ML this is done by learning features of a data training data set, i.e. learning hidden relations in the data. Since the learned patterns and the subsequent prediction is based on the training set, the success of ML heavily depends on the training data. This renders the application of ML to natural systems rather challenging, since natural systems' dynamics are complex and the training data set has to reflect the diverse dynamics. Typically this renders the data set huge in size ('Big Data'). Examples for applications are visual pattern recognition and language processing. Moreover, ML techniques compete with model approaches, which already built in relations between system elements and which do not depend on specific training sets. In certain research fields, such as meteorology, system models and corresponding techniques are already that powerful that a benefit of ML remains still unclear. However, recent techniques combine ML and models to achieve the best insight into the dynamics of systems.
The present Research Topic aims to collect work from a large variety of research domains on natural complex systems that provides a good up-to-date overview of the field of ML. Work on both theoretical and experimental aspects is welcome from various research disciplines such as chemistry, biology, physics, meteorology, cognitive science, physiology, psychology, medicine or neuroscience.
Keywords: model improvement, learning rules, deep learning, data assimilation, big data
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