Recent Advances in Statistical Machine Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 1171
Special Issue Editor
Interests: machine learning, including deep learning algorithms for health and insurance data; statistical methods for complex data; predictive analytics; dependence modeling; insurance ratemaking; loss reserving; bias assessment; computational algorithms; Markov chain Monte Carlo (MCMC) algorithms; expectation maximization (EM) algorithms; longitudinal and survival methods for complex data
Special Issue Information
Dear Colleagues,
With the rapid growth of data-driven applications reliant on artificial intelligence (AI), statistical machine learning emerges as a pivotal force in tackling intricate challenges spanning natural language processing, computer vision, engineering, bioinformatics, healthcare, marketing, and beyond. This Special Issue endeavors to disseminate the latest discoveries and advancements at the confluence of statistics and machine learning. We welcome submissions encompassing both theoretical advancements and practical applications, including the crafting of probabilistic models, inference algorithms, and learning techniques. Topics of interest span a wide spectrum, ranging from supervised, unsupervised, semi-supervised learning, reinforcement learning, sparse learning, dimensionality reduction, interpretable machine learning, and the realms of deep learning and neural networks. Moreover, we encourage innovative applications of such methodologies aimed at resolving emerging challenges across diverse domains.
Dr. Michelle Xia
Guest Editor
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 submissions that pass pre-check are 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. Mathematics 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 2600 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
- artificial intelligence
- deep learning
- ensemble methods
- machine learning
- neural networks
- probabilistic modeling
- reinforcement learning
- semi-supervised learning
- statistical learning
- sparse learning
- supervised learning
- unsupervised learning
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