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Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


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Machine learning in complex networks / Thiago Christiano Silva and Liang Zhao

By: Contributor(s): Material type: TextTextPublication details: Cham : Springer, 2016.Description: xviii, 331 pages : illustrations (some color) ; 25 cmISBN:
  • 9783319172897
Subject(s): DDC classification:
  • 006.31 23 C555
Contents:
1. Introduction -- 2. Complex Networks -- 3. Machine Learning -- 4. Network Construction Techniques -- 5. Network-Based Supervised Learning -- 6. Network-Based Unsupervised Learning -- 7. Network-Based Semi-Supervised Learning -- 8. Case Study of Network-Based Supervised Learning: High-Level Data Classification -- 9. Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks -- 10. Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks
Summary: This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 006.31 C555 (Browse shelf(Opens below)) Available 137892
Total holds: 0

Includes bibliographical references and index.

1. Introduction --
2. Complex Networks --
3. Machine Learning --
4. Network Construction Techniques --
5. Network-Based Supervised Learning --
6. Network-Based Unsupervised Learning --
7. Network-Based Semi-Supervised Learning --
8. Case Study of Network-Based Supervised Learning: High-Level Data Classification --
9. Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks --
10. Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks

This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

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