Información de la conferencia
ICDMML 2019: International Conference on Data Mining and Machine Learning
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Día de Entrega:
2018-12-30
Fecha de Notificación:
2019-01-15
Fecha de Conferencia:
2019-04-29
Ubicación:
Hong Kong, China
Vistas: 20669   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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Revistas Relacionadas
CCFNombre CompletoFactor de ImpactoEditorISSN
BioData Mining6.1Springer1756-0381
Mechanism and Machine Theory5.3Elsevier0094-114X
Journal of Computing in Civil Engineering5.2ASCE0887-3801
Journal of Computer Assisted Learning4.6Wiley-Blackwell0266-4909
Simulation Modelling Practice and Theory4.6Elsevier1569-190X
bData Mining and Knowledge Discovery4.3Springer1384-5810
Language Learning & Technology4.1University of Hawaii Press1094-3501
Minds and Machines3.4Springer0924-6495
bMachine Learning2.9Springer0885-6125
Computing in Science & Engineering1.9IEEE1521-9615