The Joint Framework for Dynamic Topic Semantic Link Network Prediction
To explore the maximum potential of textual data, a well-organized dynamic semantic structure of the topics is in fact of great importance for effectively supporting the advanced intelligent application. The proposed framework joints the Gaussian mixture model and the Bayesian network to conduct inf...
Main Authors: | Anping Zhao, Lingling Zhao, Yu Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8590797/ |
Similar Items
-
Bayesian Mixture Model for Prediction of Bus Arrival Time
by: Misbahuddin, et al.
Published: (2015-12-01) -
Opportunistic Networks Link Prediction Method Based on Bayesian Recurrent Neural Network
by: Yuliang Ma, et al.
Published: (2019-01-01) -
Research on Data Link Ontology Mapping Algorithm Based on Bayesian Network Model
by: Chunhui Yuan, et al.
Published: (2019-01-01) -
Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network
by: Yuanqiao Wen, et al.
Published: (2019-02-01) -
Layer-Wise Network Compression Using Gaussian Mixture Model
by: Eunho Lee, et al.
Published: (2021-01-01)