Special Issue "Machine Learning and Data Mining in Pattern Recognition"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 October 2020.

Special Issue Editors

Prof. Dr. Zeev Volkovich
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Guest Editor
Department of Software Engineering, ORT Braude College, Karmiel, 21982, Israel
Interests: probability theory; machine learning; pattern recognition; data mining; knowledge discovery; neural networks and artificial intelligence
Prof. Dr. Oleg Granichin
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Guest Editor
Department of System Programming, Saint Petersburg State University, 199034 Saint Petersburg, Russia
Interests: estimation and optimization; multiagent adaptive control; randomized algorithms; clustering; data mining
Dr. Dvora Toledano-Kitai
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Guest Editor
Ort Braude College of Engineering, Karmiel, 21982, Israel
Interests: global optimization; data mining

Special Issue Information

Dear Colleagues

Technological progress brings to our attention substantial datasets that need to be studied with suitable speed and capacity. As a result, currently developed machine learning and pattern recognition tools have undergone rapid expansion in the computer science frontier and with respect to applications in engineering, industry, cyber-physics, economics, medicine, biology, the social and political world, and other areas. As two facets of the same area, machine learning and pattern recognition share approaches, methods, and skills constructed and adapted to resolve new knowledge extraction problems and play a key role in artificial intelligence systems. 

This Special Issue focuses on advances in the modeling of systems and machine learning applications. Topics of interest include, but are not limited to, the following: natural language processing; bioinformatics; social sciences applications; mathematics; operations research; distributed optimization; multi-agent technology; deep learning; and big data paradigms.

Prof. Dr. Zeev Volkovich
Prof. Dr. Oleg Granichin
Dr. Dvora Toledano-Kitai
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 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. Mathematics 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 1200 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

  • Pattern recognition
  • Deep learning
  • Machine learning
  • Big data
  • Optimization

Published Papers (1 paper)

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Research

Open AccessArticle
Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models
Mathematics 2020, 8(7), 1119; https://doi.org/10.3390/math8071119 - 08 Jul 2020
Abstract
We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing [...] Read more.
We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining in Pattern Recognition)
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