Some small countries do it better [[electronic resource] ] : rapid growth and its causes in Singapore, Ireland, and Finland / / Shahid Yusuf, Kaoru Nabeshima

• Record Nr.: UNINA9910461722203321
• Autore: Yusuf Shahid <1949->
• Pubbl/distr/stampa: Washington, D.C., : World Bank, c2012
• ISBN: 1-283-49174-5; 9786613491749; 0-8213-8925-4
• Descrizione fisica: 1 online resource (188 p.)
• Altri autori (Persone): NabeshimaKaoru
• Disciplina: 338.9
• Soggetti: States, Small - Economic conditions; Economic development - Singapore; Economic development - Ireland; Economic development - Finland; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; Preface and Acknowledgments; About the Authors; Abbreviations; Chapter 1. Looking for Growth; Growth: The Stylized Facts; Hunting for Quarks; Looking at the "Sifire" Family of Models; References; Chapter 2. How Sifire Compressed Development; Globalization: The Vital Backdrop; Old Light; Crises and Consequences; Investing in Growth; Learning and Innovation; Creative Cities; What Sifire Got Right; References; Chapter 3. Elements of a Learning Economy; Changes in Economic Structure; The Rise of High-Tech Exports; Education Sector: An Overview; Innovation Capabilities; Innovation Facilitator Concluding Observations; References; Chapter 4. Governance and Growth; Coordinators; Institutions for Growth; Power of Urban Networks; References; Chapter 5. Delivering Quality Education; The Power of Quality Education; Vocational Training; Sustaining Consistent Performance; Starting Almost from Scratch: What It Takes; References; Chapter 6. The Message from Sifire; Pragmatic Governance; Leveraging Global Markets and General-Purpose ; Technologies; Updating the Sifire Experience; Quality of Human Capital; Implications for African Countries; References; Appendix A; Appendix B Index; Figures; 1.1 Sources of Economic Growth by Region, 1989-95 and 1995-2003; 1.2 Percentage Point Contribution of Real Net Exports to GDP Growth in Asia, 1995-2000 and 2000-06; 3.1 Finland's Industrial Composition, 1970-2007; 3.2 Ireland's Industrial Composition, 1970-2007; 3.3 Singapore's Industrial Composition, 1970-2007; 3.4 Finland's Composition of Exports by Technology Class, 1970 and 2000; 3.5 Ireland's Composition of Exports by Technology Class, 1970 and 2000; 3.6 Singapore's Composition of Exports by Technology Class, 1970 and 2000; 3.7 Average Years of Schooling for People 15-Years-Old and Above, 1960-2000 3.8 Public Expenditure per Student; 3.9 Composition of Graduates in Finland, Tertiary Education, 1981-99; 3.10 Openness to Trade, 2001-08; 3.11 Imports of Goods and Services, 2001-08; 3.12 Net Foreign Direct Investment Flows, 1980-2008; 3.13 Royalty Payments, 1975-2007; 3.14 Royalty Payments as Share of GDP, 1975-2007; 4.1 Finnish Innovation System: Organizations and Coordination; 4.2 Irish Science and Technology Structure; 4.3 The EDB Network; Tables; 1.1 Average Annual GDP Growth, 1995-2001 and 2002-08; 1.2 Average GDP Per Capita, 1995-2001 and 2002-08 1.3 Average Share of Global GDP, 1995-2001 and 2002-08; 1.4 African Growth by Sector, 2002-07; 1.5 Contribution of Capital, Labor, and TFP to World Growth; 1.6 Population of the Sifire Group, 1985 and 2008; 1.7 GDP Per Capita of the Sifire Group, 1985 and 2008; 1.8 Average Annual GDP Growth of the Sifire Group, 1985-2008; 1.9 IMD World Competitiveness Rankings of the Sifire Group, 1989 and 1992; 1.10 World Economic Forum and IMD Competitiveness Rankings of the Sifire Group, 2003; 3.1 Finland's Top 10 Exports in 1970, by Value; 3.2 Finland's Top 10 Exports in 2000, by Value
• Sommario/riassunto: Countries worldwide are struggling to imitate the industrial prowess of the East Asian pacesetters, but growth accelerations have proven remarkably transient. Building a portfolio of tradable goods and services and steadily raising the level of investment in these activities, has generally defied the best policy efforts - in particular, bringing investment ratios on par with East Asian averages has presented the greatest challenge. Hence the search is on for growth recipes not so tightly bound to investment, to manufacturing activities, and to the export of manufactured products. In casting

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

• Record Nr.: UNINA9910483725203321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• ISBN: 3-319-46675-5
• Edizione: [1st ed. 2016.]
• Descrizione fisica: 1 online resource (XVIII, 651 p. 215 illus.)
• Collana: Theoretical Computer Science and General Issues, , 2512-2029 ; ; 9949
• Disciplina: 006.3
• Soggetti: 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
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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 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 -- 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 -- 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.
• Sommario/riassunto: 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.