Information Theoretic Learning with Its Applications
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 26
Special Issue Editors
Interests: artificial intelligence; learning technologies; machine learning; human–computer interaction; social media; affective computing; sentiment analysis;
Special Issues, Collections and Topics in MDPI journals
Interests: data structures; information retrieval; data mining; bioinformatics; string algorithmic; computational geometry; multimedia databases; internet technologies
Special Issues, Collections and Topics in MDPI journals
Interests: database and knowledge-based systems; intelligent information systems; data mining; pattern recognition; data compression; biomedical informatics; multimedia
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; data mining; knowledge discovery; data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the rapidly evolving field of data science, Information Theoretic Learning (ITL) emerges as a cornerstone for uncovering complex patterns in data through the lens of information theory. This Special Issue of Entropy, titled “Information Theoretic Learning with Its Applications”, aims to explore the frontier of ITL and its transformative applications across various disciplines. We seek contributions that push the boundaries of understanding, applying, and innovating with ITL to solve real-world problems. Topics of interest include but are not limited to information theory, entropy-based algorithms, mutual information in supervised and unsupervised learning, information bottleneck methods, and applications of ITL in various domains. Through this Special Issue, we invite researchers and practitioners to share their findings, methodologies, and insights, contributing to a comprehensive discourse on how information theory continues to shape the landscape of data analysis and learning.
Dr. Isidoros Perikos
Dr. Christos Makris
Prof. Dr. Vasileios Megalooikonomou
Dr. Sotiris Kotsiantis
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. 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 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
- information theory
- data science
- supervised learning
- unsupervised learning
- data analysis techniques
- machine learning applications
