東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning in complex networks

Christiano Silva, Thiago.

FindBook

Google Book

Amazon

博客來

Machine learning in complex networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in complex networks/ by Thiago Christiano Silva, Liang Zhao.
作者:	Christiano Silva, Thiago.
其他作者:	Zhao, Liang.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xviii, 331 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-17290-3
ISBN:	9783319172903$q(electronic bk.)

Machine learning in complex networks
Christiano Silva, Thiago.

Machine learning in complex networks[electronic resource] /by Thiago Christiano Silva, Liang Zhao. - Cham :Springer International Publishing :2016. - xviii, 331 p. :ill. (some col.), digital ;24 cm.

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.

This book explores the features and advantages offered by complex networks in the domain of machine learning. In the first part of the book, we present an overview on complex networks and machine learning. Then, we provide a comprehensive description on network-based machine learning. In addition, we also address the important network construction issue. In the second part of the book, we describe some techniques for supervised, unsupervised, and semi-supervised learning that rely on complex networks to perform the learning process. Particularly, we thoroughly investigate a particle competition technique for both unsupervised and semi-supervised learning that is modeled using a stochastic nonlinear dynamical system. Moreover, we supply an analytical analysis of the model, which enables one to predict the behavior of the proposed technique. In addition, we deal with data reliability issues or imperfect data in semi-supervised learning. Even though with relevant practical importance, little research is found about this topic in the literature. In order to validate these techniques, we employ broadly accepted real-world and artificial data sets. Regarding network-based supervised learning, we present a hybrid data classification technique that combines both low and high orders of learning. The low-level term can be implemented by any traditional classification technique, while the high-level term is realized by the extraction of topological features of the underlying network constructed from the input data. Thus, the former classifies test instances according to their physical features, while the latter measures the compliance of 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 may generate broad interests to scientific community, mainly to computer science and engineering areas.

ISBN: 9783319172903$q(electronic bk.)

Standard No.: 10.1007/978-3-319-17290-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning in complex networks
LDR:03512nmm a2200325 a 4500 001 2030228
003 DE-He213
005 20160817133217.0
006 m d
007 cr nn 008maaau
008 160908s2016 gw s 0 eng d
020 $a 9783319172903$q(electronic bk.)
020 $a 9783319172897$q(paper)
024 7 $a 10.1007/978-3-319-17290-3 $2 doi
035 $a 978-3-319-17290-3
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .C555 2016
100 1 $a Christiano Silva, Thiago. $3 2181724
245 1 0 $a Machine learning in complex networks $h [electronic resource] / $c by Thiago Christiano Silva, Liang Zhao.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xviii, 331 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a This book explores the features and advantages offered by complex networks in the domain of machine learning. In the first part of the book, we present an overview on complex networks and machine learning. Then, we provide a comprehensive description on network-based machine learning. In addition, we also address the important network construction issue. In the second part of the book, we describe some techniques for supervised, unsupervised, and semi-supervised learning that rely on complex networks to perform the learning process. Particularly, we thoroughly investigate a particle competition technique for both unsupervised and semi-supervised learning that is modeled using a stochastic nonlinear dynamical system. Moreover, we supply an analytical analysis of the model, which enables one to predict the behavior of the proposed technique. In addition, we deal with data reliability issues or imperfect data in semi-supervised learning. Even though with relevant practical importance, little research is found about this topic in the literature. In order to validate these techniques, we employ broadly accepted real-world and artificial data sets. Regarding network-based supervised learning, we present a hybrid data classification technique that combines both low and high orders of learning. The low-level term can be implemented by any traditional classification technique, while the high-level term is realized by the extraction of topological features of the underlying network constructed from the input data. Thus, the former classifies test instances according to their physical features, while the latter measures the compliance of 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 may generate broad interests to scientific community, mainly to computer science and engineering areas.
650 0 $a Machine learning. $3 533906
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Complex Networks. $3 1244825
650 2 4 $a Science, general. $3 894431
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Zhao, Liang. $3 1057459
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-17290-3
950 $a Computer Science (Springer-11645)