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Linking and Mining Heterogeneous and Multi-view Data

Linking and Mining Heterogeneous and Multi-view Data

 

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Editorial:
Springer Nature B.V.
Año de edición:
2019
Materia
Ingeniería de las comunicaciones/las telecomunicaciones
ISBN:
9783030018719

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Sobre IBD

Chapter 1. Multi-view Data Completion.- Chapter 2. Multi-view Clustering.- Chapter 3. Semi-supervised and Unsupervised Approaches to Record Pairs Classification in Multi-source Data Linkage.- Chapter 4. A Review of Unsupervised and Semi-Supervised Blocking Methods for Record Linkage.- Chapter 5. Traffic Sensing & Assessing in Digital Transportation Systems.- Chapter 6. How did the discussion go: Discourse act classification in social media conversations.- Chapter 7. Entity Linking in Enterprise Search: Combining Textual and Structural Information.- Chapter 8. Clustering Multi-view Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey Paper.- Chapter 9. Leveraging Heterogeneous Data for Fake News Detection.- Chapter 10. On the Evaluation of Community Detection Algorithms on Heterogeneous Social Media Data.- Chapter 11. General Framework for Multi-View Metric Learning.- Chapter 12. Learning from imbalanced datasets with cross-view cooperation-based ensemble methods.

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