Special Issue "Information Theory in Machine Learning and Data Science"
Deadline for manuscript submissions: closed (31 August 2017)
The field of machine learning and data science is concerned with the design and analysis of algorithms for making decisions, reasoning about the world, and knowledge extraction from massive amounts of data. On the one hand, the performance of machine learning algorithms is limited by the amount of predictively relevant information contained in the data. On the other hand, different procedures for accessing and processing data can be more or less informative. Claude Shannon originally developed information theory with communication systems in mind. However, it has also proved to be an indispensible analytical tool in the field of mathematical statistics, where it is used to quantify the fundamental limits on the performance of statistical decision procedures and to guide the processes of feature selection and experimental design.
The purpose of this Special Issue is to highlight the state-of-the-art in applications of information theory to the fields of machine learning and data science. Possible topics include, but are not limited to, the following:
- Fundamental information-theoretic limits of machine learning algorithms
- Information-directed sampling and optimization
- Statistical estimation, optimization, and learning under information constraints
- Information bottleneck methods
- Information-theoretic approaches to adaptive data analysis
- Information-theoretic approaches to feature design and selection
- Estimation of information-theoretic functionals
Prof. Dr. Maxim Raginsky
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. 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 1500 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.