Conference Information
ICDMML 2019: International Conference on Data Mining and Machine Learning
http://www.icdmml.org/Submission Date: |
2018-12-30 |
Notification Date: |
2019-01-15 |
Conference Date: |
2019-04-29 |
Location: |
Hong Kong, China |
Viewed: 19619 Tracked: 14 Attend: 1
Call For Papers
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
Last updated by Dou Sun in 2018-05-09
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Related Journals
| CCF | Full Name | Impact Factor | Publisher | ISSN |
|---|---|---|---|---|
| BioData Mining | 6.1 | Springer | 1756-0381 | |
| Mechanism and Machine Theory | 5.3 | Elsevier | 0094-114X | |
| Journal of Computing in Civil Engineering | 5.2 | ASCE | 0887-3801 | |
| Journal of Computer Assisted Learning | 4.6 | Wiley-Blackwell | 0266-4909 | |
| Simulation Modelling Practice and Theory | 4.6 | Elsevier | 1569-190X | |
| b | Data Mining and Knowledge Discovery | 4.3 | Springer | 1384-5810 |
| Language Learning & Technology | 4.1 | University of Hawaii Press | 1094-3501 | |
| Minds and Machines | 3.4 | Springer | 0924-6495 | |
| b | Machine Learning | 2.9 | Springer | 0885-6125 |
| Computing in Science & Engineering | 1.800 | IEEE | 1521-9615 |
| Full Name | Impact Factor | Publisher |
|---|---|---|
| BioData Mining | 6.1 | Springer |
| Mechanism and Machine Theory | 5.3 | Elsevier |
| Journal of Computing in Civil Engineering | 5.2 | ASCE |
| Journal of Computer Assisted Learning | 4.6 | Wiley-Blackwell |
| Simulation Modelling Practice and Theory | 4.6 | Elsevier |
| Data Mining and Knowledge Discovery | 4.3 | Springer |
| Language Learning & Technology | 4.1 | University of Hawaii Press |
| Minds and Machines | 3.4 | Springer |
| Machine Learning | 2.9 | Springer |
| Computing in Science & Engineering | 1.800 | IEEE |