LEADER 03228nam 2200637Ia 450 001 9910459505003321 005 20200520144314.0 010 $a0-470-60403-4 010 $a1-282-55170-1 010 $a9786612551703 010 $a0-470-60402-6 035 $a(CKB)2670000000014486 035 $a(EBL)510222 035 $a(OCoLC)609861536 035 $a(SSID)ssj0000367631 035 $a(PQKBManifestationID)11269546 035 $a(PQKBTitleCode)TC0000367631 035 $a(PQKBWorkID)10341863 035 $a(PQKB)10038777 035 $a(MiAaPQ)EBC510222 035 $a(Au-PeEL)EBL510222 035 $a(CaPaEBR)ebr10375603 035 $a(CaONFJC)MIL255170 035 $a(EXLCZ)992670000000014486 100 $a20091217d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aWinning across global markets$b[electronic resource] $ehow Nokia creates strategic advantage in a fast-changing world /$fDan Steinbock 205 $a1st ed. 210 $aSan Francisco $cJossey-Bass$dc2010 215 $a1 online resource (302 p.) 300 $aDescription based upon print version of record. 311 $a0-470-33966-7 320 $aIncludes bibliographical references and index. 327 $aWINNING ACROSS GLOBAL MARKETS: How Nokia Creates Strategic Advantage in a Fast-Changing World; CONTENTS; INTRODUCTION; Chapter 1: SUCCESS THROUGH LEGACY AND GLOBALIZATION; Chapter 2: STRATEGY THROUGH THE EXECUTIVE TEAM; Chapter 3: HOW NOKIA'S VALUES, CULTURE, AND PEOPLE CONTRIBUTE TO SUCCESS; Chapter 4: BUILDING A GLOBALLY NETWORKED MATRIX ORGANIZATION; Chapter 5: INNOVATING GLOBALLY VIA R&D NETWORKS; Chapter 6: DEVELOPING STRATEGIC CAPABILITIES ACROSS THE WORLD; Chapter 7: HOW NOKIA IS GROWING AND TRANSFORMING ITS BUSINESS AREAS 327 $aChapter 8: COMPETING IN GLOBAL MARKETS: The Rise of Large Emerging EconomiesChapter 9: HOW NOKIA SEEKS TO SUSTAIN LEADERSHIP; NOKIA'S KEY EXECUTIVES; NOTES; ACKNOWLEDGMENTS; ABOUT THE AUTHOR; INDEX 330 $aLessons for attaining global competitiveness, one market at a time, from international business giant Nokia Winning Across Global Markets examines how 145-year-old Nokia grew from a paper mill in Finland to a multinational telecommunications leader. Why are Nokia's lessons critical for other companies and industries? While multinationals based in large countries benefit from inherent advantages--such as a home base that often accounts for 30 to 50 percent of their revenues--multinationals based in smaller countries such as Nokia, enjoy no such competitive edge. Nokia, in fact, 606 $aCell phone equipment industry$zFinland$xManagement 606 $aCell phone systems 606 $aTelecommunication$xManagement 608 $aElectronic books. 615 0$aCell phone equipment industry$xManagement. 615 0$aCell phone systems. 615 0$aTelecommunication$xManagement. 676 $a338.7/62138456 700 $aSteinbock$b Dan$0920642 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910459505003321 996 $aWinning across global markets$92064839 997 $aUNINA LEADER 06496nam 2200505 450 001 996525671803316 005 20230731000140.0 010 $a9783031301056$b(electronic bk.) 010 $z9783031301049 024 7 $a10.1007/978-3-031-30105-6 035 $a(MiAaPQ)EBC7236701 035 $a(Au-PeEL)EBL7236701 035 $a(OCoLC)1376446116 035 $a(DE-He213)978-3-031-30105-6 035 $a(PPN)269655174 035 $a(EXLCZ)9926435286900041 100 $a20230731d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNeural information processing $e29th international conference, ICONIP 2022, virtual event, November 22-26, 2022, proceedings, Part I /$fedited by Mohammad Tanveer [and four others] 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer Nature Switzerland AG,$d[2023] 210 4$dİ2023 215 $a1 online resource (660 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13623 311 08$aPrint version: Tanveer, Mohammad Neural Information Processing Cham : Springer International Publishing AG,c2023 9783031301049 320 $aIncludes bibliographical references and index. 327 $aTheory and Algorithms -- Solving Partial Differential Equations using Point-based Neural Networks -- Patch Mix Augmentation with Dual Encoders for Meta-Learning -- Tacit Commitments Emergence in Multi-agent Reinforcement Learning -- Saccade Direction Information Channel -- Shared-Attribute Multi-Graph Clustering with Global Self-Attention -- Mutual Diverse-Label Adversarial Training -- Multi-Agent Hyper-Attention Policy Optimization -- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks -- FPD: Feature Pyramid Knowledge Distillation -- An effective ensemble model related to incremental learning in neural machine translation -- Local-Global Semantic Fusion Single-shot Classification Method -- Self-Reinforcing Feedback Domain Adaptation Channel -- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data -- Additional Learning for Joint Probability Distribution Matching in BiGAN -- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection -- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields -- Partial Label learning with Gradually Induced Error-Correction Output Codes -- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer -- Heterogeneous Graph Representation for Knowledge Tracing -- Intuitionistic fuzzy universum support vector machine -- Support vector machine based models with sparse auto-encoder based features for classification problem -- Selectively increasing the diversity of GAN-generated samples -- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning -- Differentiable Causal Discovery Under Heteroscedastic Noise -- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels -- Adaptive Scaling for U-Net in Time Series Classification -- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples -- SSPR: A Skyline-Based Semantic Place Retrieval Method -- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification -- Adaptive Tabu Dropout for Regularization of Deep Neural Networks -- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization -- Nearest Neighbor Classifier with Margin Penalty for Active Learning -- Factual Error Correction in Summarization with Retriever-Reader Pipeline -- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning -- Self-attention based multi-scale graph convolutional networks -- Synesthesia Transformer with Contrastive Multimodal Learning -- Context-based Point Generation Network for Point Cloud Completion -- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks -- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer -- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering -- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning -- A fast and efficient algorithm for filtering the training dataset -- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection -- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem -- Boosting Graph Convolutional Networks With Semi-Supervised Training -- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs -- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning -- An Analytical Estimation of Spiking Neural Networks Energy Efficiency -- Correlation Based Semantic Transfer with Application to Domain Adaptation -- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network -- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction. 330 $aThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22?26, 2022. The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13623 606 $aNeural computers$vCongresses 606 $aNeural networks (Computer science)$vCongresses 615 0$aNeural computers 615 0$aNeural networks (Computer science) 676 $a006.3 702 $aTanveer$b Mohammad 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996525671803316 996 $aNeural Information Processing$92554499 997 $aUNISA