Statistical Machine Learning with Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 8259
Special Issue Editors
Interests: intelligent maintenance; decision optimization; power system reliability
Special Issues, Collections and Topics in MDPI journals
Interests: combinatorial optimization; stochastic programming; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent decades have witnessed the explosive growth of machine learning methodologies powered by continuous advancements in fundamental statistical theories and frontier data science, including big data, cloud computing, and the Internet of Things. Statistical machine learning, as a typical data-driven multidisciplinary technology, focuses mainly on the processing, analysis, and prediction of multi-source data through the formulation and optimization of probabilistic statistical models. To date, such technology has been extensively applied to diverse critical science domains, such as data mining, signal processing, pattern recognition, computer vision, natural language processing, biometrics, quality process control, risk analysis, lifetime diagnosis and prognosis, DNA sequencing, speech/handwriting recognition, and robotics, with tremendous, well-justified achievements.
This Special Issue is devoted to a collection of the latest theoretical advancements and industrial applications with regard to statistical machine learning, covering but not limited to the research topics outlined below:
- Statistical modeling and inference
- Analysis and optimization of probabilistic models
- Processing and analysis of big data
- Deep learning and its applications
- Transfer learning and its applications
- Reinforcement learning and its applications
- Data-driven uncertainty quantification
- Evolutionary computation and intelligent algorithms
- Modeling and monitoring of smart manufacturing systems
- The Bayesian approach and Bayesian networks
- The fusion of machine learning and domain knowledge
Dr. Li Yang
Dr. Yuchen Li
Dr. Mukund Nilakantan Janardhanan
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. 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
- statistical inference
- probabilistic model
- deep learning
- neural network
- reinforcement learning
- Bayesian approach
- intelligent optimization