Machine Learning in Complex Networks

Name: Machine Learning in Complex Networks
Brand: Springer International Publishing AG
SKU: 9783319792347
Price: 42378 HUF
Availability: InStock

Szerző: Thiago Christiano Silva, Liang Zhao

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer International Publishing AG

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

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This book presents the features and advantages offered by complex networks in the machine learning d...

Információk a könyvről

Szerző

Thiago Christiano Silva, Liang Zhao

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2018

oldal

331

EAN

9783319792347

Enbook ID

20092434

Kiadó

Springer International Publishing AG

Súly

539

Méretek

155 x 19 x 20

Teljes leírás

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 this book, 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.

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Machine Learning in Complex Networks

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