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Linking and Mining Heterogeneous and Multi-view Data

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Cover of 'Linking and Mining Heterogeneous and Multi-view Data'

Table of Contents

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    Book Overview
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    Chapter 1 Multi-View Data Completion
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    Chapter 2 Multi-View Clustering
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    Chapter 3 Semi-supervised and Unsupervised Approaches to Record Pairs Classification in Multi-Source Data Linkage
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    Chapter 4 A Review of Unsupervised and Semi-supervised Blocking Methods for Record Linkage
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    Chapter 5 Traffic Sensing and Assessing in Digital Transportation Systems
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    Chapter 6 How Did the Discussion Go: Discourse Act Classification in Social Media Conversations
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    Chapter 7 Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods
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    Chapter 8 Entity Linking in Enterprise Search: Combining Textual and Structural Information
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    Chapter 9 Clustering Multi-View Data Using Non-negative Matrix Factorization and Manifold Learning for Effective Understanding: A Survey Paper
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    Chapter 10 Leveraging Heterogeneous Data for Fake News Detection
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    Chapter 11 General Framework for Multi-View Metric Learning
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    Chapter 12 On the Evaluation of Community Detection Algorithms on Heterogeneous Social Media Data
Attention for Chapter 7: Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods
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Chapter title
Learning from Imbalanced Datasets with Cross-View Cooperation-Based Ensemble Methods
Chapter number 7
Book title
Linking and Mining Heterogeneous and Multi-view Data
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-01872-6_7
Book ISBNs
978-3-03-001871-9, 978-3-03-001872-6
Authors

Cécile Capponi, Sokol Koço, Capponi, Cécile, Koço, Sokol

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