Machine Learning Meets Large-Scale Model: Current Trends and Future Challenges
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 321
Special Issue Editors
Interests: machine learning; pattern recognition; stochastic optimization; large-scale models
Interests: next-generation network; biological informatization; smart education
Interests: communication signal processing; mobile communication; wireless communication; network security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Pattern recognition and classification are fundamental tasks in machine learning and computer vision. In recent years, many innovative discoveries and significant advancements in pattern recognition and classification have been made. Since supervised learning, semi-supervised learning, and ensemble methods are the main approaches of machine learning, it is necessary to explore their performance in differentiated scenarios and solve challenges regarding their practical application, such as efficient image prior modelling, fast and robust large-scale optimization algorithms, etc., especially under large-scale model backgrounds.
In this Special Issue of Electronics, we seek to publish original and creative contributions in the field of supervised and semi-supervised learning (including other advanced approaches) for pattern recognition and classification. Research papers with theoretical, technical, and/or practical approaches, and review articles, are all welcome. Topics of interest include, but are not limited to:
- Design of novel classifiers (such as deep neural networks, attention mechanisms, capsule networks, etc.);
- Few-shot learning;
- Explainable supervisory models;
- Multimodal/multilabel classification;
- Robust classification methods (adversarial attack defense, noisy label learning);
- Graph-based semi-supervised learning;
- The combination of active learning and semi-supervised learning;
- Domain adaptation and transfer learning;
- Time series data classification;
- The application of contrastive learning in semi-supervised learning;
- Classification methods for specific scenarios such as biomedicine, remote sensing, etc.
Dr. Zhuang Yang
Prof. Dr. Sai Zou
Dr. Yanqun Tang
Guest Editors
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. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- neural networks
- interpretable classification
- advanced machine learning algorithms
- generative models
- large-scale optimization
- few-shot learning
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