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Artificial neural networks and machine learning -- ICANN 2022 : 31st international conference on artificial neural networks, Bristol, UK, September 6-9, 2022, proceedings, Part IV / / edited by Elias Pimenidis [and four others]
Artificial neural networks and machine learning -- ICANN 2022 : 31st international conference on artificial neural networks, Bristol, UK, September 6-9, 2022, proceedings, Part IV / / edited by Elias Pimenidis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (817 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Machine learning
ISBN 3-031-15937-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part IV -- Analysing the Predictivity of Features to Characterise the Search Space -- 1 Introduction -- 2 Related Work -- 3 Landscape Features -- 4 Experimental Results -- 4.1 Feature Exploratory Analysis -- 4.2 Operator Classification -- 5 Conclusions and Future Work -- References -- Boosting Feature-Aware Network for Salient Object Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Overall Framework -- 3.2 Edge Guidance Sub-network -- 3.3 Object Sub-network -- 3.4 Loss Function -- 4 Experimental Results -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with the State-of-the-Arts -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Continual Learning Based on Knowledge Distillation and Representation Learning -- 1 Introduction -- 2 Related Works -- 2.1 Class Incremental Learning -- 2.2 Beta-VAE -- 2.3 Knowledge Distillation -- 3 Model and Methodology -- 3.1 KRCL Model -- 3.2 KRCL Loss Function -- 3.3 Model Parameters and Update Rules -- 4 Experimental Comparison -- 4.1 Benchmark Datasets -- 4.2 Baseline Methods -- 4.3 Network Architecture -- 4.4 Evaluation Metrics -- 4.5 Experimental Results and Analysis -- 5 Conclusions and Future Works -- References -- Deep Feature Learning for Medical Acoustics -- 1 Introduction -- 2 The Considered Frontends -- 2.1 Mel-filterbanks -- 2.2 LEAF -- 2.3 nnAudio -- 3 Models -- 3.1 EfficientNet -- 3.2 VGG -- 4 Datasets -- 4.1 Respiratory Dataset -- 4.2 Heartbeat Dataset -- 5 Experiments -- 5.1 Pre-processing -- 5.2 System Parameterization -- 6 Results -- 6.1 Test 1 - Respiratory -- 6.2 Test 2 - Heartbeat -- 6.3 Overall -- 7 Conclusion -- References -- Feature Fusion Distillation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Feature Fusion Module -- 3.2 Asymmetric Switch Function.
3.3 Total Loss Function -- 4 Experiments -- 4.1 Image Classification (CIFAR-100) -- 4.2 Image Classification (ImageNet-1K) -- 4.3 Object Detection -- 4.4 Semantic Segmentation -- 5 Ablation Study -- 6 Conclusion -- A Margin Value -- References -- Feature Recalibration Network for Salient Object Detection -- 1 Introduction -- 2 Proposed Method -- 2.1 Consistency Recalibration Module -- 2.2 Multi-source Feature Recalibration Module -- 2.3 Loss Function -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with the State-of-the-Art -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Feature Selection for Trustworthy Regression Using Higher Moments -- 1 Introduction -- 2 Trustworthy Regression -- 3 Feature Relevance -- 3.1 Feature Relevance for Classification -- 3.2 Feature Relevance for (MSE-)Regression -- 4 Feature Selection Methods -- 5 On the Relation of Relevance Notions -- 6 Application: Moment Feature Relevance -- 7 Empirical Evaluation -- 8 Conclusion -- References -- Fire Detection Based on Improved-YOLOv5s -- 1 Introduction -- 2 Method -- 2.1 Data Collection and Preprocessing -- 2.2 Network Model -- 2.3 Cosine Annealing + Warm-Up -- 2.4 Label Smoothing -- 2.5 Multi-scale -- 3 Result -- 3.1 Evaluation Index Calculation Formula -- 3.2 Results Presentation -- 4 Discussion -- 5 Conclusion -- References -- Heterogeneous Graph Neural Network for Multi-behavior Feature-Interaction Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Heterogeneous Bipartite Graph -- 2.2 User-Features Interaction -- 2.3 Graph Neural Network Aggregation Layer -- 2.4 Prediction Layer -- 2.5 Model Training -- 2.6 Complexity Analysis -- 3 Experiment -- 3.1 Dataset Description -- 3.2 Experimental Settings -- 3.3 Overall Performance -- 3.4 Model Analysis -- 4 Conclusion -- References.
JointFusionNet: Parallel Learning Human Structural Local and Global Joint Features for 3D Human Pose Estimation -- 1 Introduction -- 2 Related Works -- 2.1 3D Human Pose Estimation -- 2.2 Global-Local Features Fusion -- 3 Method -- 3.1 Inspiring Pattern of Human Pose -- 3.2 Global and Local Features Fusion -- 4 Experiments -- 4.1 Datasets, Evaluation Metrics and Details -- 4.2 Comparison with State-of-the-Art Methods -- 4.3 Cross-dataset Results on 3DPW -- 4.4 Visualization and Explanation -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-scale Feature Extraction and Fusion for Online Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Knowledge Distillation -- 2.2 Online Knowledge Distillation -- 2.3 Multi-scale Feature -- 3 Proposed Method -- 3.1 Problem Definition -- 3.2 MFEF Framework -- 3.3 Loss Function -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 5 Conclusion -- References -- Multi-scale Vertical Cross-layer Feature Aggregation and Attention Fusion Network for Object Detection -- 1 Introduction -- 2 Related Work -- 3 Architecture of Proposed Network -- 3.1 Multi-scale Vertical Cross-Layer Feature Aggregation Network -- 3.2 Attention Fusion Module -- 3.3 Anchor Optimization Strategy -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Comparison with Other Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing -- 1 Introduction -- 2 Proposed Method -- 2.1 Overall Network Architecture -- 2.2 DCT and Channel Attention -- 2.3 Multi-spectral Channel Attention (MSCA) -- 2.4 Multi-spectral Dynamic Feature Encoding (MSDFE) -- 2.5 Loss Function -- 3 Experiments -- 3.1 Datasets and Training Details -- 3.2 Comparison with State-of-the-Arts -- 3.3 Visual Results -- 3.4 Model Parameters -- 4 Ablation Study.
4.1 Network Branches -- 4.2 Multi-spectral Dynamic Feature Encoding -- 5 Conclusion -- References -- Ranking Feature-Block Importance in Artificial Multiblock Neural Networks -- 1 Introduction -- 2 Block Importance Ranking Methods -- 2.1 Composite Strategy -- 2.2 Knock-In Strategy -- 2.3 Knock-Out Strategy -- 3 Experiments -- 3.1 Simulation Experiment -- 3.2 Real-World Experiment -- 4 Discussion -- 5 Conclusion -- References -- Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification -- 1 Introduction -- 2 The Proposed Method -- 2.1 F,1 Norm Regularization Term -- 2.2 Sensor Selection Model -- 2.3 Model Optimization -- 2.4 Complexity Analysis -- 3 Experiment -- 3.1 Data Sets -- 3.2 Experiments Settings -- 3.3 Comparison of Classification Accuracy -- 4 Conclusion and Future Work -- References -- Stimulates Potential for Knowledge Distillation -- 1 Introduction -- 2 Related Literature -- 2.1 Knowledge Distillation -- 2.2 Normalization -- 3 Approach -- 3.1 Residual-Based Local Feature Normalization -- 3.2 Local Feature Normalized Extraction -- 3.3 How to Use Structure -- 4 Experiment -- 4.1 Experiments on CIFAR-10 -- 4.2 Experiments on CIFAR-100 -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Adaptive Compatibility Matrix for Superpixel-CRF -- 1 Introduction -- 2 Related Work -- 2.1 CRF and Superpixel-CRF -- 2.2 Compatibility Function -- 3 Preliminary -- 4 Adaptive Compatibility Matrix -- 5 Apply Adaptive Compatibility Matrix to Superpixel CRF -- 5.1 Binary Class -- 5.2 Multi-class -- 6 Experiments -- 7 Conclusions -- References -- BERT-Based Scientific Paper Quality Prediction -- 1 Introduction -- 2 BERT -- 3 Proposed Quality Prediction of Scientific Papers -- 3.1 Dataset of Scientific Papers -- 3.2 Quality Classification of Papers -- 3.3 BERT-Based Model of Quality Prediction of Scientific Papers.
4 Experimental Results -- 4.1 Training in the Pre-training Phase on Abstracts from S2ORC -- 4.2 Training in the Fine-Tuning Phase -- 4.3 The Test Accuracy of Prediction of the Trained Model -- 4.4 Detailed Analysis of the Prediction -- 5 Conclusions -- References -- Effective ML-Block and Weighted IoU Loss for Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 Box Regression Loss -- 2.2 One-Stage Object Detectors -- 3 Approach -- 3.1 Weighted IoU Loss -- 3.2 MobileLight Block -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Studies -- 4.3 Evaluation on PASCAL VOC -- 4.4 Evaluation on COCOmini -- 5 Conclusion -- References -- FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Datasets and Detailed Model Implementations -- 4.1 Data Description -- 4.2 Performance Evaluation -- 4.3 Parameters for Switching -- 4.4 Model Description and Hyper-parameters -- 5 Results -- 6 Conclusion -- References -- Reject Options for Incremental Regression Scenarios -- 1 Introduction -- 2 Problem Setting -- 3 Rejection Models -- 3.1 Drift Rejection -- 3.2 Local Outlier Probabilities Rejector -- 3.3 Baseline Rejection -- 4 Experiments -- 4.1 Chaotic Time Series Data -- 4.2 Real World Data -- 4.3 RMSE-Reject Curves -- 4.4 Chaotic Data Experiment -- 4.5 Real World Data Experiment -- 5 Results -- 5.1 Chaotic Data Results -- 5.2 Real World Data Results -- 5.3 Tabular Evaluation -- 6 Conclusion -- References -- Stream-Based Active Learning with Verification Latency in Non-stationary Environments -- 1 Introduction -- 2 Related Work -- 3 Proposed Active Learning Framework -- 3.1 Proposed Utility Estimator: PRopagate Labels -- 3.2 Proposed Budget Strategy: Dynamic Budget Allocation -- 4 Experimental Setup -- 5 Results and Discussion.
6 Conclusion and Future Work.
Record Nr. UNISA-996490365203316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial neural networks and machine learning -- ICANN 2022 : 31st international conference on artificial neural networks, Bristol, UK, September 6-9, 2022, proceedings, Part IV / / edited by Elias Pimenidis [and four others]
Artificial neural networks and machine learning -- ICANN 2022 : 31st international conference on artificial neural networks, Bristol, UK, September 6-9, 2022, proceedings, Part IV / / edited by Elias Pimenidis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (817 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Machine learning
ISBN 3-031-15937-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part IV -- Analysing the Predictivity of Features to Characterise the Search Space -- 1 Introduction -- 2 Related Work -- 3 Landscape Features -- 4 Experimental Results -- 4.1 Feature Exploratory Analysis -- 4.2 Operator Classification -- 5 Conclusions and Future Work -- References -- Boosting Feature-Aware Network for Salient Object Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Overall Framework -- 3.2 Edge Guidance Sub-network -- 3.3 Object Sub-network -- 3.4 Loss Function -- 4 Experimental Results -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with the State-of-the-Arts -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Continual Learning Based on Knowledge Distillation and Representation Learning -- 1 Introduction -- 2 Related Works -- 2.1 Class Incremental Learning -- 2.2 Beta-VAE -- 2.3 Knowledge Distillation -- 3 Model and Methodology -- 3.1 KRCL Model -- 3.2 KRCL Loss Function -- 3.3 Model Parameters and Update Rules -- 4 Experimental Comparison -- 4.1 Benchmark Datasets -- 4.2 Baseline Methods -- 4.3 Network Architecture -- 4.4 Evaluation Metrics -- 4.5 Experimental Results and Analysis -- 5 Conclusions and Future Works -- References -- Deep Feature Learning for Medical Acoustics -- 1 Introduction -- 2 The Considered Frontends -- 2.1 Mel-filterbanks -- 2.2 LEAF -- 2.3 nnAudio -- 3 Models -- 3.1 EfficientNet -- 3.2 VGG -- 4 Datasets -- 4.1 Respiratory Dataset -- 4.2 Heartbeat Dataset -- 5 Experiments -- 5.1 Pre-processing -- 5.2 System Parameterization -- 6 Results -- 6.1 Test 1 - Respiratory -- 6.2 Test 2 - Heartbeat -- 6.3 Overall -- 7 Conclusion -- References -- Feature Fusion Distillation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Feature Fusion Module -- 3.2 Asymmetric Switch Function.
3.3 Total Loss Function -- 4 Experiments -- 4.1 Image Classification (CIFAR-100) -- 4.2 Image Classification (ImageNet-1K) -- 4.3 Object Detection -- 4.4 Semantic Segmentation -- 5 Ablation Study -- 6 Conclusion -- A Margin Value -- References -- Feature Recalibration Network for Salient Object Detection -- 1 Introduction -- 2 Proposed Method -- 2.1 Consistency Recalibration Module -- 2.2 Multi-source Feature Recalibration Module -- 2.3 Loss Function -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 3.3 Comparison with the State-of-the-Art -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Feature Selection for Trustworthy Regression Using Higher Moments -- 1 Introduction -- 2 Trustworthy Regression -- 3 Feature Relevance -- 3.1 Feature Relevance for Classification -- 3.2 Feature Relevance for (MSE-)Regression -- 4 Feature Selection Methods -- 5 On the Relation of Relevance Notions -- 6 Application: Moment Feature Relevance -- 7 Empirical Evaluation -- 8 Conclusion -- References -- Fire Detection Based on Improved-YOLOv5s -- 1 Introduction -- 2 Method -- 2.1 Data Collection and Preprocessing -- 2.2 Network Model -- 2.3 Cosine Annealing + Warm-Up -- 2.4 Label Smoothing -- 2.5 Multi-scale -- 3 Result -- 3.1 Evaluation Index Calculation Formula -- 3.2 Results Presentation -- 4 Discussion -- 5 Conclusion -- References -- Heterogeneous Graph Neural Network for Multi-behavior Feature-Interaction Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Heterogeneous Bipartite Graph -- 2.2 User-Features Interaction -- 2.3 Graph Neural Network Aggregation Layer -- 2.4 Prediction Layer -- 2.5 Model Training -- 2.6 Complexity Analysis -- 3 Experiment -- 3.1 Dataset Description -- 3.2 Experimental Settings -- 3.3 Overall Performance -- 3.4 Model Analysis -- 4 Conclusion -- References.
JointFusionNet: Parallel Learning Human Structural Local and Global Joint Features for 3D Human Pose Estimation -- 1 Introduction -- 2 Related Works -- 2.1 3D Human Pose Estimation -- 2.2 Global-Local Features Fusion -- 3 Method -- 3.1 Inspiring Pattern of Human Pose -- 3.2 Global and Local Features Fusion -- 4 Experiments -- 4.1 Datasets, Evaluation Metrics and Details -- 4.2 Comparison with State-of-the-Art Methods -- 4.3 Cross-dataset Results on 3DPW -- 4.4 Visualization and Explanation -- 4.5 Ablation Study -- 5 Conclusion -- References -- Multi-scale Feature Extraction and Fusion for Online Knowledge Distillation -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Knowledge Distillation -- 2.2 Online Knowledge Distillation -- 2.3 Multi-scale Feature -- 3 Proposed Method -- 3.1 Problem Definition -- 3.2 MFEF Framework -- 3.3 Loss Function -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 5 Conclusion -- References -- Multi-scale Vertical Cross-layer Feature Aggregation and Attention Fusion Network for Object Detection -- 1 Introduction -- 2 Related Work -- 3 Architecture of Proposed Network -- 3.1 Multi-scale Vertical Cross-Layer Feature Aggregation Network -- 3.2 Attention Fusion Module -- 3.3 Anchor Optimization Strategy -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Comparison with Other Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing -- 1 Introduction -- 2 Proposed Method -- 2.1 Overall Network Architecture -- 2.2 DCT and Channel Attention -- 2.3 Multi-spectral Channel Attention (MSCA) -- 2.4 Multi-spectral Dynamic Feature Encoding (MSDFE) -- 2.5 Loss Function -- 3 Experiments -- 3.1 Datasets and Training Details -- 3.2 Comparison with State-of-the-Arts -- 3.3 Visual Results -- 3.4 Model Parameters -- 4 Ablation Study.
4.1 Network Branches -- 4.2 Multi-spectral Dynamic Feature Encoding -- 5 Conclusion -- References -- Ranking Feature-Block Importance in Artificial Multiblock Neural Networks -- 1 Introduction -- 2 Block Importance Ranking Methods -- 2.1 Composite Strategy -- 2.2 Knock-In Strategy -- 2.3 Knock-Out Strategy -- 3 Experiments -- 3.1 Simulation Experiment -- 3.2 Real-World Experiment -- 4 Discussion -- 5 Conclusion -- References -- Robust Sparse Learning Based Sensor Array Optimization for Multi-feature Fusion Classification -- 1 Introduction -- 2 The Proposed Method -- 2.1 F,1 Norm Regularization Term -- 2.2 Sensor Selection Model -- 2.3 Model Optimization -- 2.4 Complexity Analysis -- 3 Experiment -- 3.1 Data Sets -- 3.2 Experiments Settings -- 3.3 Comparison of Classification Accuracy -- 4 Conclusion and Future Work -- References -- Stimulates Potential for Knowledge Distillation -- 1 Introduction -- 2 Related Literature -- 2.1 Knowledge Distillation -- 2.2 Normalization -- 3 Approach -- 3.1 Residual-Based Local Feature Normalization -- 3.2 Local Feature Normalized Extraction -- 3.3 How to Use Structure -- 4 Experiment -- 4.1 Experiments on CIFAR-10 -- 4.2 Experiments on CIFAR-100 -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Adaptive Compatibility Matrix for Superpixel-CRF -- 1 Introduction -- 2 Related Work -- 2.1 CRF and Superpixel-CRF -- 2.2 Compatibility Function -- 3 Preliminary -- 4 Adaptive Compatibility Matrix -- 5 Apply Adaptive Compatibility Matrix to Superpixel CRF -- 5.1 Binary Class -- 5.2 Multi-class -- 6 Experiments -- 7 Conclusions -- References -- BERT-Based Scientific Paper Quality Prediction -- 1 Introduction -- 2 BERT -- 3 Proposed Quality Prediction of Scientific Papers -- 3.1 Dataset of Scientific Papers -- 3.2 Quality Classification of Papers -- 3.3 BERT-Based Model of Quality Prediction of Scientific Papers.
4 Experimental Results -- 4.1 Training in the Pre-training Phase on Abstracts from S2ORC -- 4.2 Training in the Fine-Tuning Phase -- 4.3 The Test Accuracy of Prediction of the Trained Model -- 4.4 Detailed Analysis of the Prediction -- 5 Conclusions -- References -- Effective ML-Block and Weighted IoU Loss for Object Detection -- 1 Introduction -- 2 Related Work -- 2.1 Box Regression Loss -- 2.2 One-Stage Object Detectors -- 3 Approach -- 3.1 Weighted IoU Loss -- 3.2 MobileLight Block -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Ablation Studies -- 4.3 Evaluation on PASCAL VOC -- 4.4 Evaluation on COCOmini -- 5 Conclusion -- References -- FedNet2Net: Saving Communication and Computations in Federated Learning with Model Growing -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Datasets and Detailed Model Implementations -- 4.1 Data Description -- 4.2 Performance Evaluation -- 4.3 Parameters for Switching -- 4.4 Model Description and Hyper-parameters -- 5 Results -- 6 Conclusion -- References -- Reject Options for Incremental Regression Scenarios -- 1 Introduction -- 2 Problem Setting -- 3 Rejection Models -- 3.1 Drift Rejection -- 3.2 Local Outlier Probabilities Rejector -- 3.3 Baseline Rejection -- 4 Experiments -- 4.1 Chaotic Time Series Data -- 4.2 Real World Data -- 4.3 RMSE-Reject Curves -- 4.4 Chaotic Data Experiment -- 4.5 Real World Data Experiment -- 5 Results -- 5.1 Chaotic Data Results -- 5.2 Real World Data Results -- 5.3 Tabular Evaluation -- 6 Conclusion -- References -- Stream-Based Active Learning with Verification Latency in Non-stationary Environments -- 1 Introduction -- 2 Related Work -- 3 Proposed Active Learning Framework -- 3.1 Proposed Utility Estimator: PRopagate Labels -- 3.2 Proposed Budget Strategy: Dynamic Budget Allocation -- 4 Experimental Setup -- 5 Results and Discussion.
6 Conclusion and Future Work.
Record Nr. UNINA-9910592989803321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part II / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part II / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (834 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Computer engineering
Computer networks
Computers
Image processing—Digital techniques
Computer vision
Application software
Artificial Intelligence
Computer Engineering and Networks
Computing Milieux
Computer Imaging, Vision, Pattern Recognition and Graphics
Computer and Information Systems Applications
ISBN 3-031-15931-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910592980403321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part II / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part II / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (834 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Computer engineering
Computer networks
Computers
Image processing—Digital techniques
Computer vision
Application software
Artificial Intelligence
Computer Engineering and Networks
Computing Milieux
Computer Imaging, Vision, Pattern Recognition and Graphics
Computer and Information Systems Applications
ISBN 3-031-15931-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996490356103316
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (783 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Application software
Computers
Computer engineering
Computer networks
Artificial Intelligence
Computer and Information Systems Applications
Computing Milieux
Computer Engineering and Networks
ISBN 3-031-15919-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Deep Learning -- , Neural Network Theory -- Relational Learning, Reinforcement Learning -- Natural language processing, Generative Models -- Graphical Models, Recommender Systems -- Image Processing, Recurrent Networks -- Evolutionary Neural Networks -- Unsupervised Neural Networks -- Neural Network Models.
Record Nr. UNINA-9910592979303321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Artificial Neural Networks and Machine Learning – ICANN 2022 [[electronic resource] ] : 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I / / edited by Elias Pimenidis, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas, Mehmet Aydin
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (783 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Application software
Computers
Computer engineering
Computer networks
Artificial Intelligence
Computer and Information Systems Applications
Computing Milieux
Computer Engineering and Networks
ISBN 3-031-15919-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Deep Learning -- , Neural Network Theory -- Relational Learning, Reinforcement Learning -- Natural language processing, Generative Models -- Graphical Models, Recommender Systems -- Image Processing, Recurrent Networks -- Evolutionary Neural Networks -- Unsupervised Neural Networks -- Neural Network Models.
Record Nr. UNISA-996490365903316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXV, 563 p. 166 illus.)
Disciplina 004
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Computer communication systems
Algorithms
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Computer Communication Networks
Algorithm Analysis and Problem Complexity
ISBN 3-319-98443-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Knowledge Engineering and Semantic Web -- Social Network Analysis -- Recommendation Methods and Recommender Systems -- Agents and Multi-Agent Systems -- Text Processing and Information Retrieval -- Sensor Networks and Internet of Things -- Data Mining Methods and Applications.
Record Nr. UNISA-996466338603316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part II / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part II / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXV, 521 p. 171 illus.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Application software
Special purpose computers
Artificial Intelligence
Algorithm Analysis and Problem Complexity
Computer Appl. in Administrative Data Processing
Special Purpose and Application-Based Systems
ISBN 3-319-98446-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Decision Support and Control Systems -- Cooperative Strategies for Decision Making and Optimization -- Complex Decision Systems -- Machine Learning in Real-World Data -- Intelligent Sustainable Smart Cities -- Computer Vision Techniques.
Record Nr. UNISA-996466339103316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXV, 563 p. 166 illus.)
Disciplina 004
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Application software
Information storage and retrieval
Computer communication systems
Algorithms
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Systems Applications (incl. Internet)
Information Storage and Retrieval
Computer Communication Networks
Algorithm Analysis and Problem Complexity
ISBN 3-319-98443-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Knowledge Engineering and Semantic Web -- Social Network Analysis -- Recommendation Methods and Recommender Systems -- Agents and Multi-Agent Systems -- Text Processing and Information Retrieval -- Sensor Networks and Internet of Things -- Data Mining Methods and Applications.
Record Nr. UNINA-9910349413003321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part II / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Computational Collective Intelligence [[electronic resource] ] : 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part II / / edited by Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawiński
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (XXV, 521 p. 171 illus.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Algorithms
Application software
Special purpose computers
Artificial Intelligence
Algorithm Analysis and Problem Complexity
Computer Appl. in Administrative Data Processing
Special Purpose and Application-Based Systems
ISBN 3-319-98446-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Decision Support and Control Systems -- Cooperative Strategies for Decision Making and Optimization -- Complex Decision Systems -- Machine Learning in Real-World Data -- Intelligent Sustainable Smart Cities -- Computer Vision Techniques.
Record Nr. UNINA-9910349412903321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui