
Machine Learning in Complex Networks
Springer (Publisher)
Published on 11. February 2016
Book
Hardback
XVIII, 331 pages
978-3-319-17289-7 (ISBN)
Description
This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in thisbook, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.
Reviews / Votes
"The book explores the combined area of complex network-based machine learning. It presents the theoretical concepts underlying the two complementary parts as well as those related to their interaction with respect to supervised, unsupervised and semi-supervised learning. The latest developments in the field, together with real-world test scenarios, are additionally treated in detail." (Catalin Stoean, zbMATH 1357.68003, 2017)More details
Edition
1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
7 s/w Abbildungen, 80 farbige Abbildungen
XVIII, 331 p. 87 illus., 80 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 25 mm
Weight
694 gr
ISBN-13
978-3-319-17289-7 (9783319172897)
DOI
10.1007/978-3-319-17290-3
Schweitzer Classification
Other editions
Additional editions

Thiago Christiano Silva | Liang Zhao
Machine Learning in Complex Networks
Book
03/2018
Springer
€117.69
Shipment within 10-15 days

Thiago Christiano Silva | Liang Zhao
Machine Learning in Complex Networks
E-Book
01/2016
Springer
€106.99
Available for download
Content
Introduction.- Complex Networks.- Machine Learning.- Network Construction Techniques.- Network-Based Supervised Learning.- Network-Based Unsupervised Learning.- Network-Based Semi-Supervised Learning.- Case Study of Network-Based Supervised Learning: High-Level Data Classification.- Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks.- Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks.