Overview
- This book provides an introduction to broad learning, focusing on the fundamental concepts, learning tasks, and methodologies to build learning models for data fusion, and knowledge discovery.
- It examines how the introduced broad learning approaches can be applied for effective data fusion and knowledge discovery on online social networks.
- The book Introduces the social network alignment task and learning algorithms based on three different learning settings.
- It provides a comprehensive introduction to the several well-known knowledge discovery problems with the fused information from multiple online social networks
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Table of contents (12 chapters)
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Front Matter
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Background Introduction
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Front Matter
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Information Fusion: Social Network Alignment
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Front Matter
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Broad Learning: Knowledge Discovery Across Aligned Networks
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Front Matter
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Future Directions
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Front Matter
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Keywords
About this book
Authors and Affiliations
About the authors
Philip S. Yu is Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.
Bibliographic Information
Book Title: Broad Learning Through Fusions
Book Subtitle: An Application on Social Networks
Authors: Jiawei Zhang, Philip S. Yu
DOI: https://doi.org/10.1007/978-3-030-12528-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-12527-1Published: 28 June 2019
Softcover ISBN: 978-3-030-12530-1Published: 14 August 2020
eBook ISBN: 978-3-030-12528-8Published: 08 June 2019
Edition Number: 1
Number of Pages: XV, 419
Number of Illustrations: 23 b/w illustrations, 81 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Data Structures, Information Systems Applications (incl. Internet), Probability and Statistics in Computer Science