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007			cr nn 008mamaa
008			140628s2015 gw | s |||| 0|eng d
020			_a9783319069388
024	7		_a10.1007/978-3-319-06938-8 _2doi
040			_bspa _dES-MaUEC
050		4	_aQ325.5 _bL674 2015 EB
100	1		_aLopes, Noel. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/98623896/
245	1	0	_aMachine Learning for Adaptive Many-Core Machines - A Practical Approach _cby Noel Lopes, Bernardete Ribeiro.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XX, 241 páginas 112 ilustraciones, 4 ilustraciones a color.)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aStudies in Big Data, _x2197-6503 ; _v7
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Supervised Learning -- Unsupervised and Semi-supervised Learning -- Large-Scale Machine Learning.
520	3		_aThe overwhelming data produced everyday and the increasing performance and cost requirements of applications is transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together.
988			_aEBSPRINGER_2018
650		7	_aAprendizaje automático _2embne _9166090
700	1		_aRibeiro, Bernardete. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n2005068056 _1http://viaf.org/viaf/99190602/
776	0	8	_iEdición impresa: _z9783319069395
776	0	8	_iEdición impresa: _z9783319069371
776	0	8	_iEdición impresa: _z9783319380964
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-06938-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI