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Mathematics
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Research on Machine Learning, Data Mining, Natural Language Processes, and...
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Research on Machine Learning, Data Mining, Natural Language Processes, and Optimization Methods

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 785

Special Issue Editor

Dr. Anabel Fraga

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Universidad Carlos III de Madrid, 28911 Madrid, Spain
Interests: semantic interoperability; systems and software engineering; knowledge engineering

Special Issue Information

Dear Colleagues,

Artificial Intelligence is a wide and hot topic in applied mathematics nowadays. It is of interest not only to algorithms but also to methods and methodologies for achieving an ethical application in the current world.

This Special Issue welcomes papers presenting new results and methods in the areas of machine learning, data science, natural language processing, and semantic interoperability, as well as applications of them. Review articles will also be considered.

Dr. Anabel Fraga
Guest Editor

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. 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

  • data mining
  • optimization
  • machine learning
  • natural language processing
  • semantic interoperability
  • patterns in data science

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Published Papers (1 paper)

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Research

22 pages, 551 KB  
Article
A Readability-Driven Curriculum Learning Method for Data-Efficient Small Language Model Pretraining
by Suyun Kim, Jungwon Park and Juae Kim
Mathematics 2025, 13(20), 3300; https://doi.org/10.3390/math13203300 - 16 Oct 2025
Viewed by 259
Abstract
Large language models demand substantial computational and data resources, motivating approaches that improve the training efficiency of small language models. While curriculum learning methods based on linguistic difficulty measures have been explored as a potential solution, prior approaches that rely on complex linguistic [...] Read more.
Large language models demand substantial computational and data resources, motivating approaches that improve the training efficiency of small language models. While curriculum learning methods based on linguistic difficulty measures have been explored as a potential solution, prior approaches that rely on complex linguistic indices are often computationally expensive, difficult to interpret, or fail to yield consistent improvements. Moreover, existing methods rarely incorporate the cognitive and linguistic efficiency observed in human language acquisition. To address these gaps, we propose a readability-driven curriculum learning method based on the Flesch Reading Ease (FRE) score, which provides a simple, interpretable, and cognitively motivated measure of text difficulty. Across two dataset configurations and multiple curriculum granularities, our method yields consistent improvements over baseline models without curriculum learning, achieving substantial gains on BLiMP and MNLI. Reading behavior evaluations also reveal human-like sensitivity to textual difficulty. These findings demonstrate that a lightweight, interpretable curriculum design can enhance small language models under strict data constraints, offering a practical path toward more efficient training. Full article
(This article belongs to the Special Issue Research on Machine Learning, Data Mining, Natural Language Processes, and Optimization Methods)
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