Información de la conferencia
ICDMML 2019: International Conference on Data Mining and Machine Learning
http://www.icdmml.org/Día de Entrega: |
2018-12-30 |
Fecha de Notificación: |
2019-01-15 |
Fecha de Conferencia: |
2019-04-29 |
Ubicación: |
Hong Kong, China |
Vistas: 11276 Seguidores: 14 Asistentes: 1
Solicitud de Artículos
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
Última Actualización Por Dou Sun en 2018-05-09
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Nombre Completo | Factor de Impacto | Editor |
---|---|---|
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 | |
Advances in Optical Technologies | Hindawi | |
Complex Adaptive Systems Modeling | Springer | |
Combinatorial Chemistry & High Throughput Screening | Bentham | |
International Journal of Control Theory and Computer Modeling | AIRCC | |
Transport and Telecommunication Journal | Transport and Telecommunication Institute |
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