Algorithms for Machine Learning and Pattern Recognition Tasks
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 23299
Special Issue Editors
Interests: ocean intelligent computing; digital vision technology; human-machine interaction
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; deep learning; pattern recognition; modeling behavioral and physiological human data; human activity and gesture recognition; handwriting and voice analysis; human mobility analysis; biometrics; human–computer interaction; detection and assessment of neurodegenerative diseases from biometric signals
Special Issues, Collections and Topics in MDPI journals
Interests: human activity recognition; modeling physiological functions; emotions recognition; affective and social interaction; human–computer interaction; pervasive and ubiquitous environments; Internet of Things; e-health
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Pattern recognition, the automatic recognition of patterns in the input data for solving different kinds of tasks, is a mature research field with more than 50 years of active research, which has resulted in the development of real-life successful applications such as speech recognition, handwritten mail sorting, medical imaging and natural language processing.
Pattern recognition is witnessing currently a spectacular development. The reason for which is sevenfold: the breakthrough in deep and representation learning has not only led to significantly improved performance, but it has also allowed breakthroughs in new pattern recognition. Beyond the usual dichotomy of supervised learning and classification vs. unsupervised learning and data mining/knowledge discovery in databases, significant advances have been achieved in research areas, such as self-supervised learning, hybrid deep reinforcement learning, pattern mining and graph neural networks. Moreover, while pattern recognition has been associated mainly with machine learning over the last few decades, symbolic AI and expert systems have also recently attracted increasing attention, especially with the advances in neural-symbolic computing.
This Special Issue aims to gather recent advances in algorithms for pattern recognition, particularly advanced machine/deep learning, as well as symbolic, AI techniques—investigated in the context of different tasks of classification, prediction or knowledge discovery. In addition, this Special Issue seeks to bring together academics and industrials to contribute and discuss the latest research and innovations in this field.
Prof. Dr. Hui Yu
Prof. Dr. Mounim A. El Yacoubi
Prof. Dr. Mehdi Ammi
Guest Editors
Manuscript Submission Information
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Keywords
- Pattern recognition;
- Supervised and unsupervised learning;
- Self-supervised learning, reinforcement learning;
- Classification, clustering, prediction;
- Data mining, knowledge discovery in databases;
- Artificial intelligence and machine learning;
- Deep learning, CNN, RNN (LSTM, GRU, etc.), transformer models;
- Transfer learning;
- Explainable and attentional models;
- Adversarial attacks and robust models;
- Robustness of neural networks;
- AI fairness;
- Computer graphics, signal processing, bioinformatics, NLP, information retrieval;
- Bayesian models;
- Ensemble learning;
- Model fusion;
- Review of recent development in trends.
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