Theme of the Workshop

Recently, machine learning is affecting the lives of people from moment to moment. This techniques have been proven and widely used in several domains. Multimedia has emerged as one key area for the application of machine learning techniques, which involves special considerations the data is typically of very high dimension. Due to the widespread practicability, there are several important aspects to apply various machine learning on multimedia, such as over-­fitting/under-­fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data. The 1st International Workshop on Machine Learning on Multimedia Applications (MLMA 2025) will serve as a forum for researchers and technologists to discuss the state-of-the-art, exchange their experiences, present their contributions and original ideas, and set future directions in in all aspects of machine learning and technologies on multimedia applications.

Organization Committee
Program Committee Co-chairs

Yi-Cheng Chen, National Central University, Taiwan
Yan Liu, Hong Kong Polytechnic University, Hong Kong
Lin Hui, Tamkang University, Taiwan
Sheng-Chih Chen, National ChengChi University, Taiwan

Program Committee Members

Philip S. Yu, University of Illinois at Chicago, USA
Jiannong Cao, Hong Kong Polytechnic University, Hong Kong
Vincent S. Tseng, National Chiao Tung University, Taiwan
Keith Chan, Hong Kong Polytechnic University, Hong Kong
Joshua Zhexue Huang, Shenzhen University, China
Yan Huang, University of North Texas, USA
Wang-Chien Lee, Pennsylvania State University, USA
Xiaohua Tony Hu, Drexel University, USA
Wei-Shinn Ku, Auburn University, USA
Xiang Zhang, Case Western Reserve University, USA
Dai Bing Tian, Singapore Management University, Singapore
Meng-Fen Chiang, Singapore Management University, Singapore

Workshop Scope

The topics of interest related to this workshop include, but are not limited to:

• Learning algorithm on multimedia
  - Unsupervised learning
  - Supervised learning

• Dimensionality reduction
  - Principal Component Analysis
  - Independent Component Analysis
  - Self-­Organizing Maps
  - Multi-­Dimensional Scaling

• Deep learning technology work structure
  - Model optimization
  - Learning rate tuning

• Data processing
  - Data Collection
  - Data Cleaning
  - Big data

• Problem on implementation

• Applications
  - Social Network, Recommendation System
  - Mobility, Sensor Network
  - Bioinformatics
  - E-Commerce
  

Paper Submission

Papers submitted must be formatted in PDF files, IEEE Computer Society Press Format, NOT longer than 6 pages

Workshop proceedings will be included and indexed in the Communications in Computer and Information Science (CCIS)

Submission & Format information: https://easychair.org/conferences/?conf=mlma2025

Workshop Contact Person

Yi-Cheng Chen, National Central University, Taiwan
Email: ycchen@mgt.ncu.edu.tw