000			04421nam a22004095i 4500
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003			ES-MaUEC
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007			cr nn nnnaamaa
008			181112s2019 gw a o |||| 0|eng d
020			_a9783030015206 _9
024	7		_a10.1007/978-3-030-01520-6 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ334 _b2019 EB
245	0	0	_aProceedings of ELM-2017 _cedited by Jiuwen Cao, Chi Man Vong, Yoan Miche, Amaury Lendasse.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (VII, 340 páginas) _b 130 ilustraciones
347			_atext file _bPDF
490	0		_aProceedings in Adaptation Learning and Optimization _x2363-6084 _v10
505	0		_aAdaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine -- Sparse representation feature for facial expression recognition -- Protecting User Privacy in Mobile Environment using ELM-UPP -- Application Study of Extreme Learning Machine in Image Edge Extraction -- A Normalized Mutual Information Estimator Compensating Variance Fluctuations -- Reconstructing Bifurcation Diagrams of Induction Motor Drives using an Extreme Learning Machine -- Ensemble based error minimization reduction forELM -- The Parameter Updating Method Based onKalman Filter for Online Sequential ExtremeLearning Machine -- Extreme Learning Machine BasedShip Detection Using Synthetic Aperture Radar.
520	3		_aThis book contains some selected papers from the International Conference on Extreme Learning Machine (ELM) 2017, held in Yantai, China, October 4-7, 2017. The book covers theories, algorithms and applications of ELM. 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. 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. It gives readers a glance of the most recent advances of ELM.
650		7	_aInteligencia artificial _xCongresos y asambleas _2embne
700			_aCao, Jiuwen _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998413
700	1		_aVong, Chi Man. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMiche, Yoan. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aLendasse, Amaury _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _998416
710	2		_aSpringerLink (Online service) _9106996
776	0	8	_iPrinted edition: _z9783030015190
776	0	8	_iPrinted edition: _z9783030015213
776	0	8	_iPrinted edition: _z9783030131821
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-01520-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b09/2019 _cm _dz _ea _feng _ggw _h0
999			_c111281 _d111281 _x1