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020 _a9783319172903
_9978-3-319-17290-3
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
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072 7 _aCOM004000
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082 0 4 _a006.3
_223
100 1 _aChristiano Silva, Thiago.
_eauthor.
245 1 0 _aMachine Learning in Complex Networks
_h[recurso electrónico] /
_cby Thiago Christiano Silva, Liang Zhao.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVIII, 331 p. 87 illus., 80 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
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.
650 0 _aComputer science.
650 0 _aScience.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aPattern recognition.
650 0 _aPhysics.
650 0 _aComputational intelligence.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputational Intelligence.
650 2 4 _aComplex Networks.
650 2 4 _aScience, general.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aPattern Recognition.
700 1 _aZhao, Liang.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319172897
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-17290-3
912 _aZDB-2-SCS
942 _cLIBRO_ELEC
999 _c226443
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