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000753600 019__ $$a936627310
000753600 020__ $$a9783319172903$$q(electronic book)
000753600 020__ $$a3319172905$$q(electronic book)
000753600 020__ $$z9783319172897
000753600 0247_ $$a10.1007/978-3-319-17290-3$$2doi
000753600 035__ $$aSP(OCoLC)ocn936371491
000753600 035__ $$aSP(OCoLC)936371491$$z(OCoLC)936627310
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000753600 049__ $$aISEA
000753600 050_4 $$aQ325.5
000753600 08204 $$a006.3/1$$223
000753600 1001_ $$aChristiano Silva, Thiago,$$eauthor.
000753600 24510 $$aMachine learning in complex networks$$h[electronic resource] /$$cThiago Christiano Silva, Liang Zhao.
000753600 264_1 $$aCham :$$bSpringer,$$c2016.
000753600 300__ $$a1 online resource (xviii, 331 pages) :$$billustrations.
000753600 336__ $$atext$$btxt$$2rdacontent
000753600 337__ $$acomputer$$bc$$2rdamedia
000753600 338__ $$aonline resource$$bcr$$2rdacarrier
000753600 504__ $$aIncludes bibliographical references and index.
000753600 5050_ $$aIntroduction -- 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.
000753600 506__ $$aAccess limited to authorized users.
000753600 520__ $$aThis 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.
000753600 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 2, 2016).
000753600 650_0 $$aMachine learning.
000753600 7001_ $$aZhao, Liang,$$cDr.,$$eauthor.
000753600 77608 $$iPrint version:$$z9783319172897
000753600 852__ $$bebk
000753600 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-17290-3$$zOnline Access$$91397441.1
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000753600 980__ $$aEBOOK
000753600 980__ $$aBIB
000753600 982__ $$aEbook
000753600 983__ $$aOnline
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