Overview
- Authors:
-
-
Thiago Christiano Silva
-
Central Bank of Brazil, Departament of Research (Depep), Distrito Federal, Brazil
-
Liang Zhao
-
Computer Science and Mathematics, University of São Paulo (USP), Ribeirão Preto, Brazil
- This book combines two important and popular research areas: complex networks and machine learning
- This book contains not only fundamental background, but also recent research results
- Numerous illustrative figures and step-by-step examples help readers to understand the main idea and implementation details
- Includes supplementary material: sn.pub/extras
Access this book
Other ways to access
Table of contents (10 chapters)
-
Front Matter
Pages i-xviii
-
- Thiago Christiano Silva, Liang Zhao
Pages 1-13
-
- Thiago Christiano Silva, Liang Zhao
Pages 15-70
-
- Thiago Christiano Silva, Liang Zhao
Pages 71-91
-
- Thiago Christiano Silva, Liang Zhao
Pages 93-132
-
- Thiago Christiano Silva, Liang Zhao
Pages 133-141
-
- Thiago Christiano Silva, Liang Zhao
Pages 143-180
-
- Thiago Christiano Silva, Liang Zhao
Pages 181-205
-
- Thiago Christiano Silva, Liang Zhao
Pages 207-240
-
- Thiago Christiano Silva, Liang Zhao
Pages 241-290
-
- Thiago Christiano Silva, Liang Zhao
Pages 291-321
-
Back Matter
Pages 323-331
About this book
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
“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)
Authors and Affiliations
-
Central Bank of Brazil, Departament of Research (Depep), Distrito Federal, Brazil
Thiago Christiano Silva
-
Computer Science and Mathematics, University of São Paulo (USP), Ribeirão Preto, Brazil
Liang Zhao
