NSTL国家科技图书文献中心

1. FedGES: A Federated Learning Approach for Bayesian Network Structure Learning NSTL国家科技图书文献中心

Pablo Torrijos | Jose A. Gamez ... - 《Discovery Science,Part II》 - International Conference on Discovery Science - 2025, - 83～98 - 共16页

摘要：Bayesian Network (BN) structure learning | only evolving network* structures, not parameters or* | , using structural fusion* to combine the limited models* | controlled structural fusion* is also proposed to enhance* | traditionally centralizes data, raising privacy concerns when

关键词： Federated learning | Bayesian network structure learning | Bayesian network fusion/aggregation

2. A Ring-Based Distributed Algorithm for Learning High-Dimensional Bayesian Networks NSTL国家科技图书文献中心

Jorge D. Laborda | Pablo Torrijos ... - 《Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 17th European Conference, ECSQARU 2023, Arras, France, September 19-22, 2023, Proceedings》 - European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty - 2024, - 123～135 - 共13页

摘要：Learning Bayesian* Networks (BNs) from high* | -dimensional data is a complex and time-consuming task | . Although there are approaches based on horizontal | (instances) or vertical (variables) partitioning in the | literature, none can guarantee the same theoretical

关键词： Bayesian network learning | Bayesian network fusion/aggregation | Distributed machine learning

3. Distributed fusion-based algorithms for learning high-dimensional Bayesian Networks: Testing ring and star topologies NSTL国家科技图书文献中心

Laborda J.D. | Torrijos P.... - 《International journal of approximate reasoning》 - 2024,175(Dec.) - 1.1～1.18 - 共18页

摘要：© 2024 Elsevier Inc.Learning Bayesian* Networks* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although there are approaches | based on horizontal (instances) or vertical (variables | ) partitioning in the literature, none can guarantee the same

关键词： Bayesian Network fusion/aggregation | Bayesian Network learning | Distributed machine learning

4. Parallel structural learning of Bayesian networks: Iterative divide and conquer algorithm based on structural fusion NSTL国家科技图书文献中心

Laborda J.D. | Torrijos P.... - 《Knowledge-based systems》 - 2024,296(Jul.19) - 1.1～1.19 - 共19页

摘要：? 2024 The Author(s)Learning Bayesian* Networks* | of BN fusion* to aggregate the networks learned* | , and at each step, the last aggregated network* is* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although the literature includes

关键词： Bayesian network fusion/aggregation | Bayesian network learning | Distributed machine learning | High-dimensional problems

5. Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants NSTL国家科技图书文献中心

Gianluca Tabella | Domenico Ciuonzo ... - 《IEEE internet of things journal》 - 2024,11(8) - 13231～13246 - 共16页

摘要：This work proposes a data fusion* approach for* | approaches are proposed, each exploiting different network | local decisions to a fusion* center (FC). The FC* | provides a global decision after spatial aggregation* of* | aggregation directed at quickest detection, together with a

关键词： Wireless sensor networks | Location awareness | Monitoring | Internet of Things | Reliability | Maintenance engineering | Computer architecture

6. Implementing link prediction in protein networks via feature fusion models based on graph neural networks NSTL国家科技图书文献中心

Zhang, Chi | Gao, Qian ... - 《Computational biology and chemistry》 - 2024,108 - ARTN 107980～ - 共12页

摘要： graph sampling and aggregation* network that* | focus on analyzing the network* topology, resulting in* | network data. By seamlessly integrating gene ontology | -dimensional features. Feature fusion* is achieved through the* | , and a Bayesian* optimization strategy was applied to*

关键词： Link prediction | Protein-protein interaction networks | GraphSAGE | Graph attention networks

7. Data fusion method for wireless sensor network based on machine learning EI 工程索引 NSTL国家科技图书文献中心

Wu, Mi - 《Journal of computational methods in sciences and engineering》 - 2023,23(1) - 361～373 - 共13页

摘要： network data, the Bayesian inference method in machine | fusion, a machine learning based data fusion method for | establishment and training of wireless sensor network* model* | wireless sensor network* data, and the multi-dimensional* | de aggregation* class analysis algorithm is used to*

关键词： Machine learning | wireless sensor network | data fusion | compressed sensing | DCS method | Bayesian reasoning

8. Deep Unfolding Network for Efficient Mixed Video Noise Removal NSTL国家科技图书文献中心

Lu Sun | Yichen Wang ... - 《IEEE Transactions on Circuits and Systems for Video Technology》 - 2023,33(9) - 4715～4727 - 共13页

摘要： noise. Moreover, the design of network* architectures* | unfolding network* for the more challenging and realistic* | degraded frames. In the framework of Bayesian* deep* | transformed into a deep convolutional neural network* (DCNN* | recursive fusion* strategy to exploit temporal dependencies*

关键词： Noise reduction | Adaptation models | Information filters | Filtering algorithms | AWGN | Optimization | Integrated circuit modeling

9. Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario NSTL国家科技图书文献中心

Gianluca Tabella | Domenico Ciuonzo ... - 《2021 IEEE 24th International Conference on Information Fusion: FUSION 2021, Sun City, South Africa, 1-4 November 2021, [v.1]》 - IEEE International Conference on Information Fusion - 2021, - 1～8 - 共8页

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*

关键词： Measurement | Production systems | Offshore installations | Oils | Fault detection | Statistical distributions | Industrial plants

NSTL主题词： Industrial Plants | Defect detection | oil and gas | FUSION | Fusion reactions | fusion (melting) | Fusion, biological

10. Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario NSTL国家科技图书文献中心

Gianluca Tabella | Domenico Ciuonzo ... - 《2021 IEEE 24th International Conference on Information Fusion: FUSION 2021, Sun City, South Africa, 1-4 November 2021, [v.2]》 - IEEE International Conference on Information Fusion - 2021, - 1021～1028 - 共8页

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*

摘要：Bayesian Network (BN) structure learning | only evolving network* structures, not parameters or* | , using structural fusion* to combine the limited models* | controlled structural fusion* is also proposed to enhance* | traditionally centralizes data, raising privacy concerns when

关键词： Federated learning | Bayesian network structure learning | Bayesian network fusion/aggregation

摘要：Learning Bayesian* Networks (BNs) from high* | -dimensional data is a complex and time-consuming task | . Although there are approaches based on horizontal | (instances) or vertical (variables) partitioning in the | literature, none can guarantee the same theoretical

关键词： Bayesian network learning | Bayesian network fusion/aggregation | Distributed machine learning

摘要：© 2024 Elsevier Inc.Learning Bayesian* Networks* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although there are approaches | based on horizontal (instances) or vertical (variables | ) partitioning in the literature, none can guarantee the same

关键词： Bayesian Network fusion/aggregation | Bayesian Network learning | Distributed machine learning

摘要：? 2024 The Author(s)Learning Bayesian* Networks* | of BN fusion* to aggregate the networks learned* | , and at each step, the last aggregated network* is* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although the literature includes

关键词： Bayesian network fusion/aggregation | Bayesian network learning | Distributed machine learning | High-dimensional problems

摘要：This work proposes a data fusion* approach for* | approaches are proposed, each exploiting different network | local decisions to a fusion* center (FC). The FC* | provides a global decision after spatial aggregation* of* | aggregation directed at quickest detection, together with a

关键词： Wireless sensor networks | Location awareness | Monitoring | Internet of Things | Reliability | Maintenance engineering | Computer architecture

摘要： graph sampling and aggregation* network that* | focus on analyzing the network* topology, resulting in* | network data. By seamlessly integrating gene ontology | -dimensional features. Feature fusion* is achieved through the* | , and a Bayesian* optimization strategy was applied to*

关键词： Link prediction | Protein-protein interaction networks | GraphSAGE | Graph attention networks

关键词： Machine learning | wireless sensor network | data fusion | compressed sensing | DCS method | Bayesian reasoning

关键词： Noise reduction | Adaptation models | Information filters | Filtering algorithms | AWGN | Optimization | Integrated circuit modeling

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*

关键词： Measurement | Production systems | Offshore installations | Oils | Fault detection | Statistical distributions | Industrial plants

NSTL主题词： Industrial Plants | Defect detection | oil and gas | FUSION | Fusion reactions | fusion (melting) | Fusion, biological

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*

关键词： Data fusion | Distributed detection | Maintenance | Monitoring | Reliability | Wireless sensor network

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：Bayesian Network (BN) structure learning | only evolving network structures, not parameters or | , using structural fusion to combine the limited models | controlled structural fusion is also proposed to enhance | traditionally centralizes data, raising privacy concerns when

关键词： Federated learning | Bayesian network structure learning | Bayesian network fusion/aggregation

摘要：Learning Bayesian Networks (BNs) from high | -dimensional data is a complex and time-consuming task | . Although there are approaches based on horizontal | (instances) or vertical (variables) partitioning in the | literature, none can guarantee the same theoretical

关键词： Bayesian network learning | Bayesian network fusion/aggregation | Distributed machine learning

摘要：© 2024 Elsevier Inc.Learning Bayesian Networks | (BNs) from high-dimensional data is a complex and | time-consuming task. Although there are approaches | based on horizontal (instances) or vertical (variables | ) partitioning in the literature, none can guarantee the same

关键词： Bayesian Network fusion/aggregation | Bayesian Network learning | Distributed machine learning

摘要：? 2024 The Author(s)Learning Bayesian Networks | of BN fusion to aggregate the networks learned | , and at each step, the last aggregated network is | (BNs) from high-dimensional data is a complex and | time-consuming task. Although the literature includes

关键词： Bayesian network fusion/aggregation | Bayesian network learning | Distributed machine learning | High-dimensional problems

摘要：This work proposes a data fusion approach for | approaches are proposed, each exploiting different network | local decisions to a fusion center (FC). The FC | provides a global decision after spatial aggregation of | aggregation directed at quickest detection, together with a

关键词： Wireless sensor networks | Location awareness | Monitoring | Internet of Things | Reliability | Maintenance engineering | Computer architecture

摘要： graph sampling and aggregation network that | focus on analyzing the network topology, resulting in | network data. By seamlessly integrating gene ontology | -dimensional features. Feature fusion is achieved through the | , and a Bayesian optimization strategy was applied to

关键词： Link prediction | Protein-protein interaction networks | GraphSAGE | Graph attention networks

摘要： network data, the Bayesian inference method in machine | fusion, a machine learning based data fusion method for | establishment and training of wireless sensor network model | wireless sensor network data, and the multi-dimensional | de aggregation class analysis algorithm is used to

关键词： Machine learning | wireless sensor network | data fusion | compressed sensing | DCS method | Bayesian reasoning

摘要： noise. Moreover, the design of network architectures | unfolding network for the more challenging and realistic | degraded frames. In the framework of Bayesian deep | transformed into a deep convolutional neural network (DCNN | recursive fusion strategy to exploit temporal dependencies

关键词： Noise reduction | Adaptation models | Information filters | Filtering algorithms | AWGN | Optimization | Integrated circuit modeling

摘要： decision fusion approach aimed at performing quickest | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation is performed and | according to a Bayesian criterion which exploits change

关键词： Measurement | Production systems | Offshore installations | Oils | Fault detection | Statistical distributions | Industrial plants

NSTL主题词： Industrial Plants | Defect detection | oil and gas | FUSION | Fusion reactions | fusion (melting) | Fusion, biological

摘要： decision fusion approach aimed at performing quickest | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation is performed and | according to a Bayesian criterion which exploits change

关键词： Data fusion | Distributed detection | Maintenance | Monitoring | Reliability | Wireless sensor network

4008-161-200 800-990-8900

国家科技图书文献中心

摘要：Bayesian Network (BN) structure learning | only evolving network* structures, not parameters or* | , using structural fusion* to combine the limited models* | controlled structural fusion* is also proposed to enhance* | traditionally centralizes data, raising privacy concerns when

摘要：Learning Bayesian* Networks (BNs) from high* | -dimensional data is a complex and time-consuming task | . Although there are approaches based on horizontal | (instances) or vertical (variables) partitioning in the | literature, none can guarantee the same theoretical

摘要：© 2024 Elsevier Inc.Learning Bayesian* Networks* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although there are approaches | based on horizontal (instances) or vertical (variables | ) partitioning in the literature, none can guarantee the same

摘要：? 2024 The Author(s)Learning Bayesian* Networks* | of BN fusion* to aggregate the networks learned* | , and at each step, the last aggregated network* is* | (BNs) from high-dimensional data is a complex and | time-consuming task. Although the literature includes

摘要：This work proposes a data fusion* approach for* | approaches are proposed, each exploiting different network | local decisions to a fusion* center (FC). The FC* | provides a global decision after spatial aggregation* of* | aggregation directed at quickest detection, together with a

摘要： graph sampling and aggregation* network that* | focus on analyzing the network* topology, resulting in* | network data. By seamlessly integrating gene ontology | -dimensional features. Feature fusion* is achieved through the* | , and a Bayesian* optimization strategy was applied to*

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*

摘要： decision fusion* approach aimed at performing quickest* | production system. Specifically, a sensor network | equipment and reports the collected decisions to a fusion | center. Therein, a spatial aggregation* is performed and* | according to a Bayesian* criterion which exploits change*