Special Issue "Statistical Machine Learning for Multimodal Data Analysis"
Deadline for manuscript submissions: closed (1 November 2020).
Interests: machine learning; artificial intelligence; multimedia; intelligent systems; pervasive computing
Special Issues, Collections and Topics in MDPI journals
Special Issue in Sensors: Advances in Machine Learning for Intelligent Engineering Systems and Applications II
Special Issue in Education Sciences: Intelligence and Analytics in Education
Special Issue in Entropy: Interactive Artificial Intelligence and Man-Machine Communication
Methods and algorithms in statistical machine learning explore relationships between variables in large, complex datasets in supervised, unsupervised or semi-supervised manners. Significant research results have been presented in recent years on a variety of topics, including linear and nonlinear regression, classification, clustering, resampling methods, model selection, and regularization. Furthermore, the latest strides in deep, reinforcement, and adversarial learning in conjunction with increasing availability of data from a wide variety of modalities (visual, thermal, hyperspectral, audio/speech, textual, radar, network traffic, energy, Channel State Information, and others) provide great opportunities and at the same time significant challenges for theoretical advancements and novel practical developments in a variety of application domains. This Special Issue solicits original research papers as well as review articles and short communications in the above-described areas. Topics of interest include, without being limited to, the following:
- Statistical machine learning and pattern recognition techniques for fusion and/or understanding of multimodal, multisensorial, and/or heterogeneous data;
- Deep learning and reinforcement learning for multimodal data and signal analysis;
- Generative adversarial networks for multimodal data analysis;
- Optimization methods for training of statistical models and tuning of hyperparameters;
- Quantitative evaluation, comparison and benchmarking of statistical learning methods;
- Statistical methods for handling class imbalance and data irregularities;
- Explainability and interpretability in statistical machine learning;
- Applications of statistical machine learning in real-world problems.
Dr. Athanasios Voulodimos
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Entropy is an international peer-reviewed open access monthly 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 1800 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.
- Statistical machine learning
- Pattern recognition
- Deep learning
- Reinforcement learning
- Adversarial learning
- Hyperparameter optimization
- Multisensorial data fusion and analysis
- Multimodal signal processing
- Explainability and interpretability in machine learning