000			03958nam a22004455c 4500
988			_aSpringer_Robotics_2020
999			_c115378 _d115378 _x1
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005			20240111050158.0
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008			190629s2020 gw a o |||| 0|eng d
020			_a9783030233075
024	7		_a10.1007/978-3-030-23307-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ334 _b2020 EB
245	0	0	_aProceedings of ELM 2018 _cedited by Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (VIII, 347 páginas) _b109 ilustraciones, 79 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aProceedings in Adaptation Learning and Optimization _x2363-6084 _v11
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
520	3		_aThis book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21-23, 2018. This conference provided 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 enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental "learning particles" filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, 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. The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning. This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM. .
650		7	_2embne _aInteligencia artificial _xCongresos y asambleas _9413115
650		7	_2embne _aAprendizaje automático _xCongresos y asambleas _9166090
700			_aCao, Jiuwen _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _998413
700	1		_aVong, Chi Man _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMiche, Yoan _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLendasse, Amaury _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt _998416
776	0	8	_iPrinted edition: _z9783030233068
776	0	8	_iPrinted edition: _z9783030233082
776	0	8	_iPrinted edition: _z9783030233099
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-23307-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI