Category Machine Learning and Machine Intelligence

Deadline: June 15, 2025 | Date: July 25, 2025-July 27, 2025

Venue/Country: Kyoto, Japan

Updated: 2024-10-15 16:26:13 (GMT+9)

Call For Papers - CFP

Abbreviation: MLMI 2025

Kyoto, Japan | July 25-27, 2025

The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025) will be held in Kyoto, Japan during July 25-27, 2025. MLMI 2025 is organized by Ritsumeikan University, Japan.

Insightful presentations, engaging discussions, vibrant networking – MLMI 2025 has it all. With leading academics on the scientific committee of the event, the program is guaranteed to address the most relevant topics in the field of machine learning and machine intelligence. It's an opportunity to source feedback on your research, to get published in conference proceedings, and to explore the beautiful city Osaka, Japan.

Publication

Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published in the Conference Proceedings of MLMI 2025. (*All the previous Conference Proceedings have been archived in ACM Digital Library and have been indexed by Ei Compendex & Scopus.)

Topics

Topics of interest for submission include, but are not limited to

- Artificial Neural Networks

- Genetic Algorithms

- Association Rule Learning

- Inductive Logic Programming

- Automata, Logic and Games

- Intelligent Systems

- Bayesian Networks

- Lambda Calculus and Types

- Clustering

- Logic and Proof

- Commercial Software

- Machine Learning

- Commercial Software with Open-Source Editions

- Models of Computation

- Complex Systems

- Object-Oriented Programming

- Computational Complexity

- Open-Source Software

- Computational Learning Theory

- Pattern Recognition

- Computational Linguistics

- Reinforcement Learning

- Computer Animation

- Representation Learning

- Computer Science

- Similarity and Metric Learning

- Computer System

- Sparse Dictionary Learning

- Concurrent Algorithms and Data Structures

- Supervised Learning

- Data Mining

- Support Vector Machines

- Decision Tree Learning

- Unsupervised Learning

- Deep Learning