000			03122nam a22004095i 4500
001			300649
003			MX-SnUAN
005			20170705134251.0
007			cr nn 008mamaa
008			150903s2010 gw | o |||| 0|eng d
020			_a9783642025327 _99783642025327
024	7		_a10.1007/9783642025327 _2doi
035			_avtls000353370
039		9	_a201509030519 _bVLOAD _c201405060319 _dVLOAD _y201402180943 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ334-342
100	1		_aYeung, Daniel S. _eautor _9330306
245	1	0	_aSensitivity Analysis for Neural Networks / _cby Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W. Y. Ng.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010.
300			_aviii, 86 páginas 24 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		_aNatural Computing Series, _x1619-7127
500			_aSpringer eBooks
505	0		_ato Neural Networks -- Principles of Sensitivity Analysis -- Hyper-Rectangle Model -- Sensitivity Analysis with Parameterized Activation Function -- Localized Generalization Error Model -- Critical Vector Learning for RBF Networks -- Sensitivity Analysis of Prior Knowledge1 -- Applications.
520			_aArtificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aCloete, Ian. _eautor _9338184
700	1		_aShi, Daming. _eautor _9338185
700	1		_aNg, Wing W. Y. _eautor _9338186
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783642025310
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-02532-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c300649 _d300649