Statistical learning and Its Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 1861
Special Issue Editors
Interests: mixture models; distribution theory; EM algorithm; univariate; multivariate and copula-based claim frequency
Special Issue Information
Dear Colleagues,
The past decade has seen the emergence of a range of new technologies and big data analytics which have begun to reshape the landscape of predictive modeling in practice and in research in many areas.
Statistical learning combines computational statistics with machine learning techniques and can provide excellent predictive performances without the tedious procedure of feature engineering and learn non-linearities in the input data and interactions between these. Thus, it can enable researchers to develop a framework for analyzing the stylized characteristics of data sets across an abundance of different areas, such as biology, epidemiology, criminology, meteorology, seismology, sports science, decarbonization, forecasting of weather-related hazards due to climate change, finance, and insurance.
This Special Issue aims to showcase novel applications of the most recent statistical learning techniques for developing better data-driven methods on studying practical problems in the aforementioned areas. Special emphasis should be given to hybrid models, which are based on combinations of neural networks (NNs) with statistical models. Hybrid models can have spectacular applications due to their ability to model large data sets with a large number of input features and because they can directly deal with unstructured data instead of structuring, or aggregating, which results in losing individual information. An outstanding issue is understanding how they overcome the caveat of dimensionality to generate or classify data.
Dr. Dimitrios Christopoulos
Dr. George Tzougas
Guest Editors
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. Algorithms 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 1600 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 learning
- computational statistics
- machine learning
- data-driven methods
