WhenApr 28, 2019 - Apr 30, 2019
WhereHong Kong
Submission DeadlineMar 29, 2019
Final Version DueMar 29, 2019

Call For Papers

2019 International Conference on Data Mining and Machine Learning (ICDMML 2019) will be held on April 29 - 30 2019 in Hong Kong. The symposium will focus on the frontier topics in the theoretical and applied Data Mining, Machine Learning and AI subjects. 

Basic Information 
Paper Review 
All submissions to the ICDMML will be sent to at least 2 reviewers and evaluated based on originality, technical and research content, relevance to conference, contributions, and readability. The full paper submissions will be chosen based on technical merit, interest, applicability, and how well they fit a coherent and balanced technical program. 

Important Dates 
28th February 2019 to March 29th 2019 
Conference Date: April 29-30 2019 

The accepted papers will be published by ACM International Conference Proceedings Series. The ISBN number assigned to ICDMML 2019 is 978-1-4503-6090-6. 

Press will submit the proceedings to Ei Compendex, Scopus for indexing. 

CFP link: http://www.icdmml.org/cfp.html 

1 Artificial Intelligence 
including the following topics but not limited to 
Artificial Intelligence 
Biometric Identification 
Biocomputing and Bioinformatics 
Computational Intelligence 
Cognitive Processing 
Computer Vision 
Deep learining 
Document Recognition and Understanding 
Humanoid Robot 
Intelligent Information Processing 
Intelligent Modeling and Control Theory 
Intelligent Vehicle 
Intelligent Video Surveillance 
Machine Learning 
Mass Information Processing 
Multimedia Information Processing 
Nature Language Processing 
Nonlinear System 
Pattern Recognition 
Quantum Computation and Quantum Information 
Space Robot 
Speech and Character Recognition 
Signal Processing 
Unmanned Aircraft 
Word Recognition 

2 Data Mining 
including the following topics but not limited to 
Abnormality and data detection 
Algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains 
Big data analytic and High performance implementations of data mining algorithms 
Developing a unifying theory of data mining 
Distributed data mining and mining multi-agent data 
Mining high speed data streams 
Mining in networked settings: web, social and computer networks, and online communities 
Mining sequences and sequential data 
Mining sensor data 
Mining spatial and temporal datasets 
Mining textual and unstructured datasets 
Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis) 

3 Machine Learning 
including the following topics but not limited to 
Active learning 
Computational learning theory 
Distance measurement learning 
Deep learning 
Incremental learning and online learning 
Integrated learning 
Limit learning 
Machine learning new theory 
Manifold learning 
Multi - task learning 
Multi - sign learning 
Reinforcement learning 
Manifold learning 
Semi-supervised learning 

Submission link: http://www.icdmml.org/submission.html 

Contact Us: 

Url: www.ICDMML.org 
HK Tel: +852-5607-9095 
Tel: +86-027-59262825 
Tel: +86-18571546145 
Email: ICDMML@ieti.net

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