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001			102537
003			DE-He213
005			20240111050140.0
007			cr nn 008mamaa
008			170525s2018 gw | s |||| 0|eng d
020			_a9783319574219
024	7		_a10.1007/978-3-319-57421-9 _2doi
050			_aQ342 _b.P763 2018 EB
040			_aES-MaUEC _bspa
245	1	0	_aProceedings of ELM-2016 _cedited by Jiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XIII, 285 páginas 143 ilustraciones, 126 ilustraciones a color.)
347			_atext file _bPDF
490	0		_aProceedings in Adaptation, Learning and Optimization _x2363-6084 _v9
520	3		_aThis book contains some selected papers from the International Conference on Extreme Learning Machine 2016, which was held in Singapore, December 13-15, 2016. This conference will provide a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.  Extreme Learning Machines (ELM) aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism. ELM represents a suite of (machine or possibly biological) learning techniques in which hidden neurons need not be tuned. ELM learning theories show that very effective learning algorithms can be derived based on randomly generated hidden neurons (with almost any nonlinear piecewise activation functions), independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that "random hidden neurons" capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. ELM offers significant advantages over conventional neural network learning algorithms such as fast learning speed, ease of implementation, and minimal need for human intervention. ELM also shows potential as a viable alternative technique for large‐scale computing and artificial intelligence. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .
650		7	_aInteligencia artificial _2embne _9413115
700			_aCao, Jiuwen _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998413
700	1		_aCambria, Erik _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no2012155591 _1http://viaf.org/viaf/283846500/ _993992
700	1		_aLendasse, Amaury _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/199178500/ _998416
700	1		_aMiche, Yoan. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aVong, Chi Man. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iEdición impresa: _z9783319574202
776	0	8	_iEdición impresa: _z9783319574226
776	0	8	_iEdición impresa: _z9783319861579
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-57421-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ef _feng _ggw _h0
999			_c102537 _d102537 _x1