000			04046nam a2200445 c 4500
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988			_aSpringer_Engineering_2020
999			_c114374 _d114374 _x1
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008			190904s2020 gw a o |||| 0|eng d
020			_a9783030224752
024	7		_a10.1007/978-3-030-22475-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
245	0	0	_aSupervised and unsupervised learning for data science _cedited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (VIII, 187 páginas) _b55 ilustraciones, 45 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aUnsupervised and Semi-Supervised Learning _x2522-848X
490	0		_aEngineering (Springer-11647)
505	0		_aChapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science -- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints -- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout -- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling -- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application -- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation -- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network -- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.
520	3		_aThis book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _9138450 _aCibernética
700	1		_aBerry, Michael W _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _937622
700	1		_aMohamed, Azlinah _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aYap, Bee Wah _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _9101013
776	0	8	_iPrinted edition: _z9783030224745
776	0	8	_iPrinted edition: _z9783030224769
776	0	8	_iPrinted edition: _z9783030224776
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-22475-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
998			_aSI _cm _dz _feng _ggw _h0