000			02887nam a22003855i 4500
001			278062
003			MX-SnUAN
005			20170705134200.0
007			cr nn 008mamaa
008			150903s2006 xxu| o |||| 0|eng d
020			_a9780387312408 _99780387312408
024	7		_a10.1007/0387312404 _2doi
035			_avtls000330918
039		9	_a201509030726 _bVLOAD _c201404120547 _dVLOAD _c201404090327 _dVLOAD _c201401311356 _dstaff _y201401301157 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQ334-342
100	1		_aNikolaev, Nikolay Y. _eautor _9301383
245	1	0	_aAdaptive Learning of Polynomial Networks : _bGenetic Programming, Backpropagation and Bayesian Methods / _cby Nikolay Y. Nikolaev, Hitoshi Iba.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_axiv, 316 páginas, _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		_aGenetic and Evolutionary Computation
500			_aSpringer eBooks
505	0		_aInductive Genetic Programming -- Tree-Like PNN Representations -- Fitness Functions and Landscapes -- Search Navigation -- Backpropagation Techniques -- Temporal Backpropagation -- Bayesian Inference Techniques -- Statistical Model Diagnostics -- Time Series Modelling -- Conclusions.
520			_aThis book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and predict well. The empirical investigations detailed here demonstrate that PNN models evolved by genetic programming and improved by backpropagation are successful when solving real-world tasks. Adaptive Learning of Polynomial Networks is a vital reference for researchers and practitioners in the fields of evolutionary computation, artificial neural networks and Bayesian inference, and for advanced-level students of genetic programming. Readers will strengthen their skills in creating efficient model representations and learning operators that efficiently sample the search space, and in navigating the search process through the design of objective fitness functions.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aIba, Hitoshi. _eautor _9301384
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387312392
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/0-387-31240-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c278062 _d278062