Manuscript Topics

As a data analysis method, pattern recognition can use machine learning algorithms to automatically recognize patterns and regularities in various data systems. In the last few years, pattern recognition has become increasingly important to machine learning systems science & engineering and to everyday life. With the development of data science, we are entering the age of big data at an unprecedented rate, and accordingly, the complexity of data systems has increased tremendously. However, the lack of corresponding technology and algorithms to handle those complex data systems has hindered the further development and application of pattern recognition. As a result, we are initiating this special issue intending to bring together the research achievements of academics and industry researchers alike. The main aim of this special issue is to promote the development of theory and methodologies in the field of pattern recognition and its applications in the complex data system. Proposed submissions should be unpublished, and original either from a methodological perspective or from an application point of view.

Potential topics include, but are not limited to the following:
• New methodologies for pattern recognition systems driven by complex data
• Theoretical aspects of pattern recognition systems driven by complex data
• Real-world applications for complex-data-derived pattern recognition
• Performance measures for pattern recognition algorithms driven by complex data
• Parallel computation in pattern recognition systems driven by complex data
• Theoretical and practical problems related to pattern recognition for complex data
• Deep learning of pattern recognition driven by complex data
• Emerging topics of pattern recognition driven by complex data

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