000			02966nam a22003735i 4500
001			291723
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2005 xxk| o |||| 0|eng d
020			_a9781846281181 _99781846281181
024	7		_a10.1007/b138856 _2doi
035			_avtls000343662
039		9	_a201509031102 _bVLOAD _c201405070515 _dVLOAD _y201402061201 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aFyfe, Colin. _eautor _9323148
245	1	0	_aHebbian Learning and Negative Feedback Networks / _cby Colin Fyfe.
264		1	_aLondon : _bSpringer London, _c2005.
300			_axviii, 383 páginas 117 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvanced Information and Knowledge Processing
500			_aSpringer eBooks
505	0		_aSingle Stream Networks -- Background -- The Negative Feedback Network -- Peer-Inhibitory Neurons -- Multiple Cause Data -- Exploratory Data Analysis -- Topology Preserving Maps -- Maximum Likelihood Hebbian Learning -- Dual Stream Networks -- Two Neural Networks for Canonical Correlation Analysis -- Alternative Derivations of CCA Networks -- Kernel and Nonlinear Correlations -- Exploratory Correlation Analysis -- Multicollinearity and Partial Least Squares -- Twinned Principal Curves -- The Future.
520			_aThe central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. Two variants are considered: the first uses a single stream of data to self-organise. By changing the learning rules for the network, it is shown how to perform Principal Component Analysis, Exploratory Projection Pursuit, Independent Component Analysis, Factor Analysis and a variety of topology preserving mappings for such data sets. The second variants use two input data streams on which they self-organise. In their basic form, these networks are shown to perform Canonical Correlation Analysis, the statistical technique which finds those filters onto which projections of the two data streams have greatest correlation. The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781852338831
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b138856 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c291723 _d291723