会议信息
ICDMML 2019: International Conference on Data Mining and Machine Learning
http://www.icdmml.org/截稿日期: |
2018-12-30 |
通知日期: |
2019-01-15 |
会议日期: |
2019-04-29 |
会议地点: |
Hong Kong, China |
浏览: 11258 关注: 14 参加: 1
征稿
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
最后更新 Dou Sun 在 2018-05-09
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全称 | 影响因子 | 出版商 |
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Networking Science | Springer | |
Journal of Management Information Systems | 7.700 | Myron E. Sharpe |
ACM Transactions on Mathematical Software | 2.700 | ACM |
Complexity | 1.700 | Hindawi |
International Journal of Grid Computing & Applications | AIRCC | |
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Combinatorial Chemistry & High Throughput Screening | Bentham | |
International Journal of Control Theory and Computer Modeling | AIRCC | |
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