Research Progress of Machine Learning and Pattern Recognition Application

Dear Colleagues,

Pattern recognition and machine learning have been proven effective in many fields, which is reflected in the unprecedented increase in the number of algorithms and models around this discipline. The huge number of different machine learning methods allows for almost any new specific pattern recognition problem to be easily matched with a more appropriate machine learning approach. The accuracy and efficiency of many models are gradually catching up with or even surpassing work done by humans, giving this discipline a bright outlook.

Algorithms and models for machine learning and pattern recognition have a wide range of research possibilities. The real-world environment is usually open and dynamic and requires new robust pattern recognition models to have the ability to reject out-of-distribution and unknown samples. Additionally, there are still many new fields urgently needing more effective machine learning algorithms, ranging from robotic navigation systems, 3D object model reconstruction, interpretable models for NLP, and transformers in vision, to name but a few. A less-explored but promising research direction is using machine learning, including supervised, unsupervised, and reinforcement learning-based algorithms, to enhance system throughput and processing capabilities.

In this Special Issue, we look for new contributions, including theoretical issues and applications of the discipline. Topics of interest include all aspects of algorithms and models for machine learning and pattern recognition, including but not limited to the following detailed list:

• Machine learning and data mining
• Open world robust pattern recognition
• Intelligent systems
• Document and media analysis
• Biometrics, human analysis, and behavior understanding
• Signal processing for pattern recognition solutions
• 2D/3D object detection and recognition
• Image and video analysis and understanding
• Machine learning for systems
• Efficient model training, inference, and serving

Dr. Qian Yin
Guest Editor

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