Conference Information

ICDMML 2019: International Conference on Data Mining and Machine Learning

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Submission Date:
2018-12-30
Notification Date:
2019-01-15
Conference Date:
2019-04-29
Location:
Hong Kong, China
Viewed: 21548   Tracked: 14   Attend: 1

Call For Papers

ICDMML 2019 (International Conference on Data Mining and Machine Learning) is an academic conference held in Hong Kong, China on 2019-04-29. The paper submission deadline is 2018-12-30. Acceptance notifications are sent on 2019-01-15.

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 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) 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
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AttenderYear
Xin Yang (359)2019
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