Important Dates
Submission Deadline:
August 20, 2026
Notification Deadline:
September 20, 2026
Registration Deadline:
October 5, 2026
MLPR 2026
The 4th International Conference on
Machine Learning and Pattern Recognition
(MLPR 2026) will be held at Kyoto, Japan
during December 4-6, 2026. MLPR 2026 is
sponsored by Ritsumeikan University, Japan.
The conference includes keynote talks,
invited talks, forums and oral and poster
presentations of author papers.
We invite submissions of papers on all
topics related to machine learning and
pattern recognition for the main conference
proceedings. All papers will be reviewed in
a double-blind process and accepted papers
will be presented at the conference.
Sponsored by:
Technical Supported by:
Call for Papers
The conference calls for high-quality, unpublished, original research papers in the theory and practice of machine learning and pattern recognition. We encourage submissions from all over the world. Topics of interest include but are not limited to:
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Track 1: Foundations of Machine Learning
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Track 4: Responsible & Trustworthy AI
Track 5: Applications of ML & Pattern Recognition
Track 6: Reinforcement Learning & Decision Intelligence
Learn More CFP Flyer Download
Conference Proceedings
Accepted
papers of MLPR 2026 will be published in
IET Conference Proceedings, which will
be included in the IET Digital Library
and IEEE Xplore and submitted to
EI Compendex and Scopus for
indexing.
Journal Recommendation
Selected registered paper can be recommended for potential publication in Intelligence & Robotics (ISSN: 2770-3541 (Online)).
Impact Factor：3.9（Q2）| CiteScore: 5.8
Indexing: ESCI，Scopus，Google Scholar，CNKI
Note: The original price is $1,800; a 20% discount on the Article Processing Charge (APC) is available for full-paper submissions to the conference.
Abstract Publication
Accepted and registered abstracts of MLPR 2026 will be published in Intelligence & Robotics which will be submitted to SCI, Scopus, Google Scholar, CNKI for indexing free of charge.
Refer to the published linkage: https://www.oaepublish.com/articles/2574-1225.2025.154
