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Record Nr. |
UNISA996465321903316 |
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Titolo |
Intelligent Data Engineering and Automated Learning – IDEAL 2017 [[electronic resource] ] : 18th International Conference, Guilin, China, October 30 – November 1, 2017, Proceedings / / edited by Hujun Yin, Yang Gao, Songcan Chen, Yimin Wen, Guoyong Cai, Tianlong Gu, Junping Du, Antonio J. Tallón-Ballesteros, Minling Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XVI, 609 p. 198 illus.) |
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Collana |
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Information Systems and Applications, incl. Internet/Web, and HCI ; ; 10585 |
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Disciplina |
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Soggetti |
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Data mining |
Pattern recognition |
Artificial intelligence |
Algorithms |
Information storage and retrieval |
Computers |
Data Mining and Knowledge Discovery |
Pattern Recognition |
Artificial Intelligence |
Algorithm Analysis and Problem Complexity |
Information Storage and Retrieval |
Computation by Abstract Devices |
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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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Note generali |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents -- Learning Convolutional Ranking-Score Function by Query Preference Regularization -- 1 Introduction -- 2 Proposed Method -- 3 Experiments -- 3.1 Data Sets and Experimental Setting -- 3.2 Results -- 4 Conclusion -- References -- Dynamic Community Detection Algorithm Based on Automatic Parameter Adjustment -- 1 Introduction -- 2 Related Works -- 3 The Detailed Description of the Algorithm -- 3.1 The Influence of Parameter |
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-- 3.2 The Two Constraints -- 3.3 The Elimination of Fragments -- 3.4 The Algorithm -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Accuracy Performance -- 4.3 Modularity Performance -- 4.4 Scalability Study -- 5 Conclusion -- References -- An Ant Colony Random Walk Algorithm for Overlapping Community Detection -- Abstract -- 1 Introduction -- 2 The Improved Ant Colony Initialization Process and Definition -- 2.1 Definitions -- 2.2 The Description of the Improved Ant Colony Initialization Process -- 3 ACRWA: An Ant Colony Random Walk Algorithm for Overlapping Community Detection -- 3.1 Concept and Calculation of Random Walk -- 3.2 Description of ACRWA -- 4 Experiments Result -- 4.1 Data Source -- 4.2 Experimental Results -- 5 Summary and Conclusion -- References -- UK - Means Clustering for Uncertain Time Series Based on ULDTW Distance -- 1 Introduction -- 2 Related Work -- 3 Improved UK-Means Clustering of Uncertain Time Series -- 3.1 The Disadvantage of UK-Means for Uncertain Time Series -- 3.2 ULDTW Distance for Uncertain Time Series -- 3.3 Optimization the Calculation of Cluster Center -- 4 Experimental -- 4.1 The Construction of Uncertain Time Series -- 4.2 Experimental Results and Analysis -- 5 Conclusion and Future Work -- References -- Predicting Physical Activities from Accelerometer Readings in Spherical Coordinate System -- 1 Introduction -- 2 The Proposed Framework. |
3 Experiments -- 3.1 Data Collection -- 3.2 Protocol -- 3.3 Results -- 4 Conclusion and Future Work -- References -- A Community Detection Algorithm Based on Jaccard Similarity Label Propagation -- Abstract -- 1 Introduction -- 2 Label Propagation Algorithm for Community Detection Based on Jaccard Similarity -- 2.1 Label Propagation Algorithm -- 2.2 Label Propagation Algorithm Based on Jaccard Similarity -- 3 Experimental Results and Analysis -- 3.1 Modularity -- 3.2 Normalized Mutual Information -- 3.3 Computational Complexity -- 4 Conclusion -- Acknowledgment -- References -- A Robust Object Tracking Method Based on CamShift for UAV Videos -- 1 Introduction -- 2 Object Tracking by CamShift -- 2.1 Principle of MeanShift -- 2.2 Principle of CamShift -- 3 The MK-KF-CamShift Algorithm -- 3.1 Improvement by KF -- 3.2 Improvement by MF -- 3.3 Algorithm Implementation -- 4 Experiments and Results Analysis -- 4.1 Algorithm Performance and Analysis -- 4.2 Experimental Comparison and Analysis -- 5 Conclusion and Future Work -- References -- Multi-output LSSVM-Based Forecasting Model for Mid-Term Interval Load Optimized by SOA and Fresh Degree Function -- 1 Introduction -- 2 Data Description and Analysis -- 2.1 Day-Type -- 2.2 Body Amenity Indicator -- 3 The SOA-FD-MLSSVM Framework -- 3.1 MLSSVM for Interval Load Forecasting -- 3.2 Hyper-parameters Optimized Algorithm: Improved Seeker Optimization Algorithm -- 3.3 The Fresh Degree Function -- 4 Numerical Experiments -- 4.1 The Benefit of Body Amenity Indicator -- 4.2 The Prediction Model with Different Optimized Methods -- 4.3 Comparison with Other Forecasting Models -- 5 Conclusion -- References -- A Potential-Based Density Estimation Method for Clustering Using Decision Graph -- Abstract -- 1 Introduction -- 2 Decision Graph. |
3 An Novel Potential-Based Density Estimation Method for Clustering Using Decision Graph -- 3.1 The Double-KNN (DKNN) Algorithm -- 3.2 Potential-Based Density Estimation Method -- 3.3 Cluster Centroids Identification -- 4 Experimental Results -- 4.1 Evaluation Criterion -- 4.2 Parameter Selection -- 4.3 Comparison of the Clustering Results -- 5 Conclusion -- Acknowledgments -- References -- Optimization of Grover's Algorithm Simulation Based on Cloud Computing -- 1 Introduction -- 2 Background -- 2.1 Grover's Algorithm -- 2.2 Advantages of Cloud Computing -- 3 Optimization -- 3.1 High |
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Compression of Memory -- 3.2 Speedup of Unitary Operation -- 3.3 Parallel Simulation of Unitary Operation -- 4 Configuration of Simulation on Cloud Computing Platform -- 5 Experiments and Performance Analysis -- 6 Conclusion -- References -- Cross-Media Retrieval of Tourism Big Data Based on Deep Features and Topic Semantics -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Cross-Media Retrieval Method of Tourism Big Data -- 3.1 Deep Representation of Tourism Texts and Images -- 3.2 Semantic Learning and Modeling Based on Deep Features and Topic Semantics -- 4 Experimental Results and Analysis -- 5 Conclusions -- Acknowledgment -- References -- Information Retrieval with Implicitly Temporal Queries -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Metholodogy -- 3.1 Analyzing Temporal Intention of the Query -- 3.2 Ranking Methods with Temporal Intentions -- 3.2.1 The Language Model -- 3.2.2 The Metric Space Model -- 4 Experiments -- 5 Conclusions and Future Work -- Acknowledgement -- References -- On the Relations of Theoretical Foundations of Different Causal Inference Algorithms -- 1 Introduction -- 2 Preliminary -- 3 Algorithmic Independence and Kolmogorov Complexity -- 4 Statistical Independence and Distance Correlation. |
5 Likelihood Estimation and Bayesian Inference -- 6 A Short Summary -- 7 Conclusions -- References -- SibStCNN and TBCNN + kNN-TED: New Models over Tree Structures for Source Code Classification -- 1 Introduction -- 2 Preliminaries -- 2.1 Abstract Syntax Trees -- 2.2 Tree-Based Convolutional Neural Network -- 3 The Proposed Approaches -- 3.1 Sibling-Subtree Convolutional Neural Network (SibStCNN) -- 3.2 Combination Models of kNN-TED and SibStCNN/TBCNN -- 4 Data Preprocessing and Experimental Setup -- 4.1 Preprocessing AST Data -- 4.2 The Dataset -- 4.3 Experimental Setup -- 5 Results and Discussion -- 6 Conclusion -- References -- A Community Detection Algorithm Based on Local Double Rings and Fireworks Algorithm -- Abstract -- 1 Introduction -- 2 The Framework of Fireworks Algorithm -- 2.1 Explosion Operator -- 3 LDRFA: A Community Detection Algorithm Based on Local Double Rings and Fireworks Algorithm -- 3.1 Concept of Node Importance -- 3.2 An Improved Fireworks Initialization Method -- 3.3 Detailed Description of LDRFA -- 4 Experiments Result and Analysis -- 4.1 Experimental Result of the Real World -- 4.2 Experimental Result of the Synthetic Benchmark Networks -- 5 Summary -- References -- Cost Sensitive Matrix Factorization for Face Recognition -- 1 Introduction -- 2 Problem Formulation -- 3 Cost Sensitive Matrix Factorization -- 3.1 The Overall Objective Function -- 3.2 Optimization -- 3.3 Classification Scenario -- 4 Experimental Results -- 4.1 Face Datasets and Experimental Settings -- 4.2 Comparing with State-of-the-art Cost Sensitive Approaches -- 4.3 The Influential Factors of CSMF -- 5 Conclusion and Future Works -- References -- Research of Dengue Fever Prediction in San Juan, Puerto Rico Based on a KNN Regression Model -- Abstract -- 1 Introduction -- 2 Data -- 2.1 Research Area -- 2.2 Data -- 3 Method -- 3.1 Normalization. |
3.2 Poisson Regression -- 3.3 KNN Regression Model -- 4 Research and Discussion -- 4.1 Correlation Analysis Results -- 4.2 Poisson Regression -- 4.3 KNN Regression Prediction Results -- 5 Conclusion -- Acknowledgement -- References -- Identification of Nonlinear System Based on Complex-Valued Flexible Neural Network -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Structure of CVFNT -- 2.2 Structure Optimization of CVFNT -- 2.3 Parameters Optimization of CVFNT -- 2.4 Fitness Function -- 2.5 Flowchart of Our Method -- 3 Experiments -- 3.1 The First Nonlinear System -- 3.2 The Second |
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Nonlinear System -- 4 Summary -- Acknowledgement -- References -- Research on the Method of Splitting Large Class Diagram Based on Multilevel Partitioning -- 1 Introduction -- 2 Construction of Large Class Diagram and the Calculation of Coupling Degree -- 2.1 The Class Diagram Generated by Reverse Engineering -- 2.2 Calculating the Coupling Degree -- 3 Class Diagram Splitting Algorithm -- 3.1 Coarsening Stage -- 3.2 Initial Partition Stage -- 3.3 Refining Stage -- 4 Experiment -- 4.1 Experimental Environment and Experimental Process -- 4.2 Experimental Result -- 5 Conclusions -- References -- Ford Motorcar Identification from Single-Camera Side-View Image Based on Convolutional Neural Network -- Abstract -- 1 Background -- 2 Materials -- 3 Convolutional Neural Network -- 4 Experiments and Results -- 4.1 Data Augmentation -- 4.2 CNN Convergence Analysis -- 4.3 Confusion Matrix -- 4.4 Comparison to State-of-the-Art Approaches -- 5 Conclusion -- Acknowledgements -- References -- Predicting Personality Traits of Users in Social Networks -- 1 Introduction -- 2 Problem Definition -- 3 Data and Observations -- 3.1 Data Collection -- 3.2 Observations -- 4 Proposed Model -- 4.1 Personality-Dependent Variable Factor Graph -- 4.2 Feature Definition -- 4.3 Model Learning. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017, held in Guilin, China, in October/November 2017. The 65 full papers presented were carefully reviewed and selected from 110 submissions. These papers provided a sample of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis. |
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