Overview
- Presents a detailed examination of the role of centrality and diversity in search
- Discusses tasks in machine learning, data mining, pattern recognition, and information retrieval
- Describes applications in social and information networks
Part of the book series: SpringerBriefs in Intelligent Systems (BRIEFSINSY)
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Table of contents (7 chapters)
Keywords
About this book
The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification.
The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition.
Authors and Affiliations
About the authors
Prof. Murty’s other publications include the Springer titles Support Vector Machines and Perceptrons, Compression Schemes for Mining Large Datasets, and Pattern Recognition: An Algorithmic Approach.
Bibliographic Information
Book Title: Centrality and Diversity in Search
Book Subtitle: Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition
Authors: M.N. Murty, Anirban Biswas
Series Title: SpringerBriefs in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-030-24713-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG, part of Springer Nature 2019
Softcover ISBN: 978-3-030-24712-6Published: 24 August 2019
eBook ISBN: 978-3-030-24713-3Published: 14 August 2019
Series ISSN: 2196-548X
Series E-ISSN: 2196-5498
Edition Number: 1
Number of Pages: XI, 94
Number of Illustrations: 12 b/w illustrations, 5 illustrations in colour
Topics: Machine Learning, Pattern Recognition