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1. |
Record Nr. |
UNISA996547957003316 |
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Titolo |
Neural Information Processing . Part V : 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings / / Mohammad Tanveer [and four others], editors |
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Pubbl/distr/stampa |
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Singapore : , : Springer, , [2023] |
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©2023 |
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ISBN |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (XXXV, 609 p. 196 illus., 173 illus. in color.) |
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Collana |
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Communications in Computer and Information Science Series ; ; Volume 1792 |
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Disciplina |
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Soggetti |
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Neural computers |
Neural networks (Computer science) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Theory and Algorithms II -- GCD-PKAug: A Gradient Consistency Discriminator-based Augmentation Method for Pharmacokinetics Time Courses -- ISP-FESAN: Improving Significant Wave Height Prediction with Feature Engineering and Self-Attention Network -- Binary Orthogonal Non-negative Matrix Factorization -- Shifted Chunk Encoder for Transformer Based Streaming End-to-End ASR -- Interpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification -- Adaptive Graph Recurrent Network for Multivariate Time Series Imputation -- Adaptive Rounding Compensation for Post-Training Quantization -- More Efficient And Locally Enhanced Transformer -- ASLEEP: A Shallow neural modEl for knowlEdge graph comPletion -- A speech enhancement method combining two-branch communication and spectral subtraction -- A fast and robust Photometric redshift forecasting method using Lipschitz adaptive learning rate -- Generating Textual Description using Modified Beam Search -- Disentangling Exploration and Exploitation in Deep Reinforcement Learning Using Contingency Awareness -- Multi-Grained Fusion Graph Neural Networks for Sequential Recommendation -- Optimal Design of Cable-Driven Parallel Robots by Particle Schemes -- UPFP-growth++: An Efficient Algorithm |
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to Find Periodic-Frequent Patterns in Uncertain Temporal Databases -- Active Learning with Weak Supervision for Gaussian Processes -- HPC based Scalable Logarithmic Kernelized Fuzzy Clustering Algorithms for Handling Big Data -- Cognitive Neurosciences -- RTS:A Regional Time Series Framework for Brain Disease Classification -- Deep Domain Adaptation for EEG-based Cross-subject Cognitive Workload Recognition -- Graph Convolutional Neural Network Based on Channel Graph Fusion for EEG Emotion Recognition -- Detecting Major Depressive Disorder by Graph Neural Network Exploiting Resting-state Functional MRI -- An Improved Stimulus Reconstruction Method for EEG-based Short-time Auditory Attention Detection -- Functional Connectivity of the Brain while Solving Scientific Problems with Uncertainty as Revealed by Phase Synchronization based on Hilbert Transform -- Optimizing pcsCPD with Alternating Rank-R and Rank-1 Least Squares: Application to Complex-Valued Multi-Subject fMRI Data -- Decoding Brain Signals with Meta-Learning -- Human Centered Computing -- Research on Answer Generation for Chinese Gaokao Reading Comprehension -- A Novel Graph Transformer Based Approach Toward Multi-hop Question Answering -- Logit Distillation via Student Diversity -- Causal connectivity transition from action observation to mentalizing network for understanding other’s action intention -- ND-NER: A Named Entity Recognition Dataset for OSINT towards the National Defense Domain -- Extractive Question Answering using Transformer-based LM -- Temporal dynamics of value integration in perceptual decisions: An EEG study -- Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph Convolutional Neural Network -- BPMCF: Behavior Preference Mapping Collaborative Filtering for Multi-Behavior Recommendation -- Neural Distinguishers on TinyJAMBU-128 and GIFT-64 -- Towards Hardware-friendly and Robust Facial Landmark Detection Method -- Few-shot Class-incremental Learning for EEG-based Emotion Recognition -- Motor Imagery BCI-based Online Control Soft Glove Rehabilitation System with Vibrotactile Stimulation -- Multi-level visual feature enhancement method for visual question answering -- Learning from Hindsight Demonstrations -- Hindsight Balanced Reward Shaping -- Emotion Recognition with Facial Attention and Objective Activation Functions -- M3S-CNN: Resting-state EEG based Multimodal and Multiscale Feature Extraction for Student Status Prediction in Class -- Towards Human Keypoint Detection in Infrared Images -- Multi-human intelligence in Instance-Based Learning -- How the Presence of Cognitive Biases in Phishing Emails Affects Human Decision-making? -- A simple memory module on reading comprehension -- Predicting Parkinson’s Disease Severity Using Patient-Reported Outcomes and Genetic Information -- Towards the Development of a Machine Learning-based Action Recognition Model to Support Positive Behavioural Outcomes in Students with Autism -- Safety Issues Investigation in Deep Learning based Chatbots Answers to Medical Advice Requests. |
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Sommario/riassunto |
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The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep |
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learning, and related fields to share their new ideas, progress, and achievements. |
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2. |
Record Nr. |
UNINA9910483725203321 |
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Titolo |
Neural Information Processing : 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III / / edited by Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XVIII, 651 p. 215 illus.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 9949 |
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Disciplina |
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Soggetti |
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Pattern recognition systems |
Computer vision |
Artificial intelligence |
Computer science |
Data mining |
Application software |
Automated Pattern Recognition |
Computer Vision |
Artificial Intelligence |
Theory of Computation |
Data Mining and Knowledge Discovery |
Computer and Information Systems Applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part III -- Time Series Analysis -- Chaotic Feature Selection and Reconstruction in Time Series Prediction -- 1 Introduction -- 2 Chaotic Feature Selection and Reconstruction -- 2.1 Cooperative Neuro-Evolution -- 3 Experiments |
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and Results -- 3.1 Problem Description -- 3.2 Experimental Design -- 3.3 Results and Discussion -- 4 Conclusions and Future Work -- References -- L1/2 Norm Regularized Echo State Network for Chaotic Time Series Prediction -- Abstract -- 1 Introduction -- 2 Echo State Networks -- 3 L1/2 Regularized Echo State Network -- 4 Simulations -- 5 Conclusions -- Acknowledgement -- References -- SVD and Text Mining Integrated Approach to Measure Effects of Disasters on Japanese Economics -- Abstract -- 1 Introduction -- 2 SVD and Text Mining Integrated Approach -- 3 Time Series Stock Data Analysis -- 4 Topic Extraction Results -- 5 Conclusion -- Acknowledgement -- References -- Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting -- Abstract -- 1 Introduction -- 2 DBN with BP Learning (The Conventional Method) -- 2.1 DBN with RBM and MLP -- 3 DBN with SGA (The Proposed Method) -- 3.1 The Structure of ANNs with SGA -- 4 Prediction Experiments and Results -- 4.1 CATS Benchmark Time Series Data -- 4.2 Optimization of Meta Parameters -- 4.3 Experiments Result -- 5 Conclusion -- Acknowledgment -- References -- Neuron-Network Level Problem Decomposition Method for Cooperative Coevolution of Recurrent Networks for Time Series Prediction -- 1 Introduction -- 2 Neuron-Network Level Problem Decomposition -- 3 Experimental Setup -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Data-Driven Approach for Extracting Latent Features from Multi-dimensional Data -- Yet Another Schatten Norm for Tensor Recovery -- 1 Introduction. |
2 Theoretical Results -- 2.1 New Norm -- 2.2 Properties -- 2.3 Tensor Recovery -- 3 Experimental Results -- 4 Conclusion -- References -- Memory of Reading Literature in a Hippocampal Network Model Based on Theta Phase Coding -- 1 Introduction -- 2 Computer Simulation -- 3 Results -- 4 Discussion -- References -- Combining Deep Learning and Preference Learning for Object Tracking -- 1 Introduction -- 2 Deep and Preference Learning Tracker -- 2.1 Deep Learning -- 2.2 Preference Learning -- 2.3 Model Update -- 3 Experiments -- 4 Conclusions -- References -- A Cost-Sensitive Learning Strategy for Feature Extraction from Imbalanced Data -- 1 Introduction -- 2 A Motivating Example -- 3 Theoretical Analysis -- 3.1 Imbalance Cost Ratio -- 3.2 Cost-Sensitive Principal Component Analysis (CSPCA) -- 3.3 Cost-Sensitive Non-negative Matrix Factorization (CSNMF) -- 3.4 Revisiting the Motivating Example -- 4 Experiments and Analysis -- 4.1 Experimental Framework -- 4.2 Analysis and Results -- 5 Conclusions and Future Work -- References -- Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering -- 1 Introduction -- 2 Nonnegative Tensor Decomposition Models -- 2.1 NTD Model -- 2.2 NTT Model -- 2.3 NTT-Tucker Model: A Hybrid of the NTD and NTT Models -- 3 NTT-HALS: Proposed Algorithm for NTT and NTT-Tucker -- 3.1 NTT-HALS for NTT -- 3.2 NTT-HALS for NTT-Tucker -- 4 Experiments -- 4.1 Multi-domain Feature Extraction from ERP Data -- 4.2 Feature Extraction and Clustering from ORL Database of Face Images -- 5 Discussion -- References -- Hyper-parameter Optimization of Sticky HDP-HMM Through an Enhanced Particle Swarm Optimization -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Method: Hyperparameter Optimization -- 4.1 Random Search -- 4.2 Particle Swarm Optimization -- 4.3 Ring-Based Particle Swarm Optimization. |
5 Experiment -- 5.1 Dataset -- 5.2 Synthetic Data Results -- 5.3 Tum Kitchen Dataset Results -- 6 Conclusion -- References -- Approximate Inference Method for Dynamic Interactions in Larger Neural Populations -- 1 Introduction -- 2 Methods -- 2.1 The State-Space Model of Neural Interactions -- 2.2 Approximation Methods for a Large-Scale Analysis |
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-- 3 Results -- 4 Conclusion -- References -- Features Learning and Transformation Based on Deep Autoencoders -- 1 Introduction -- 2 Unsupervised Transformation of the Feature Space -- 2.1 Matrix Decomposition and Normalization -- 2.2 Diffusion Maps -- 2.3 Deep Autoencoders -- 3 Topological Clustering -- 4 Experimental Results -- 5 Conclusion -- References -- t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom -- 1 Introduction -- 2 Stochastic Neighbor Embedding -- 3 t-Distributed Stochastic Neighbor Embedding -- 4 Inhomogeneous t-SNE -- 4.1 Degrees of Freedom -- 4.2 Cost Function and Its Gradient -- 4.3 Optimization -- 5 Experiments -- 5.1 Experiment 1 -- 5.2 Experiment 2 -- 6 Summary and Discussion -- References -- Topological and Graph Based Clustering Methods -- Parcellating Whole Brain for Individuals by Simple Linear Iterative Clustering -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Subjects -- 2.2 Simple Linear Iterative Clustering (SLIC) -- 2.3 Evaluation Metrics -- 3 Experimental Results -- 4 Conclusion and Future Directions -- Acknowledgements -- References -- Overlapping Community Structure Detection of Brain Functional Network Using Non-negative Matrix Factorization -- 1 Introduction -- 2 Methods and Material -- 2.1 Association Matrix Construction with NASR -- 2.2 Community Detection with SNMF -- 2.3 Data Preparation -- 2.4 Experiment -- 3 Results and Discussion -- 3.1 Simulated Data Set -- 3.2 Real Resting-State fMRI Data Set. |
4 Conclusion and Limitation -- References -- Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images -- 1 Introduction -- 2 Multi-scale Communication Between Different Algorithms -- 3 Experimental Results -- 3.1 Description of the Data -- 3.2 Results -- 4 Conclusion -- References -- Towards Ontology Reasoning for Topological Cluster Labeling -- 1 Introduction and Motivations -- 2 Related Work -- 3 Preliminaries About Ontology and Reasoning -- 4 Hybrid Approach: SOM Ontology Based Labeling -- 4.1 Topological Unsupervised Learning Step -- 4.2 Ontology Based Map Labeling -- 5 Experiments -- 5.1 Satellite Images Classification -- 6 Conclusion and Future Work -- References -- Overlapping Community Detection Using Core Label Propagation and Belonging Function -- 1 Introduction -- 2 Label Propagation Algorithm -- 2.1 Standard Label Propagation -- 2.2 Label Propagation with Dams and Core Detection -- 3 Proposed Methods for Detection of Overlapping Communities -- 3.1 Function 1: Membership Function Based on the Density -- 3.2 Function 2 Membership Function Based on the Local Clustering Coefficient -- 3.3 Proposed Community Detection Algorithms -- 4 Evaluation Measures of Community Detection Algorithm, Benchmarks, Experiments and Discussion -- 4.1 Experiments -- 4.2 Comparative Analysis -- 5 Perspectives and Conclusion -- References -- A New Clustering Algorithm for Dynamic Data -- 1 Introduction -- 2 Growing Neural Gas -- 3 A New Two-Level Clustering Algorithm for GNG -- 4 Experimental Results -- 5 Conclusions and Perspectives -- References -- Reinforcement Learning -- Decentralized Stabilization for Nonlinear Systems with Unknown Mismatched Interconnections -- 1 Introduction -- 2 Problem Statement -- 3 Decentralized Controller Design and Stability Analysis -- 3.1 Optimal Control -- 3.2 Neural Network Implementation. |
3.3 Stability Analysis -- 4 Simulation Study -- 5 Conclusion -- References -- Optimal Constrained Neuro-Dynamic Programming Based Self-learning Battery Management in Microgrids -- 1 Introduction -- 2 Problem Formulation -- 3 Iterative ADP Algorithm for Battery Management System -- 3.1 Derivations of the Iterative ADP Algorithm -- 4 Simulation Analysis -- 5 Conclusion -- References -- |
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Risk Sensitive Reinforcement Learning Scheme Is Suitable for Learning on a Budget -- 1 Introduction -- 2 Incremental Learning on a Budget -- 2.1 Kernel Regression Based Learning on a Budget -- 2.2 Kernel Replacement Algorithm -- 3 Risk Sensitive Reinforcement Learning -- 4 Experimental Results -- 4.1 Results -- 5 Conclusion -- References -- A Kernel-Based Sarsa() Algorithm with Clustering-Based Sample Sparsification -- 1 Introduction -- 2 RL and Kernel Method -- 3 Clustering-Based Selective Kernel Sarsa() -- 3.1 Clustering-Based Novelty Criterion -- 3.2 Framework of CSKS() -- 4 Experiment and Results -- 4.1 Settings of Acrobot -- 4.2 Results -- 5 Conclusion -- References -- Sparse Kernel-Based Least Squares Temporal Difference with Prioritized Sweeping -- 1 Introduction -- 2 Background -- 3 Sparse Kernel-Based Least Squares Temporal Difference with Prioritized Sweeping (PS-SKLSTD) -- 3.1 Sparse Kernel-Based Least Squares Temporal Difference -- 3.2 Kernel-Based Prioritized Sweeping -- 3.3 PS-SKLSTD Algorithm -- 4 Experimental Results -- 4.1 Puddle World -- 4.2 Cart-Pole -- 5 Conclusions -- References -- Computational Intelligence -- Vietnamese POS Tagging for Social Media Text -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Tagging Method -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Models to Compare -- 5.3 Results -- 6 Conclusion -- References -- Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks -- 1 Introduction -- 2 The HR Calculus. |
3 Conjugate Gradient Algorithms. |
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Sommario/riassunto |
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The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms. |
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