000			02961nam a22004695i 4500
001			978-0-387-31240-8
003			DE-He213
005			20250710083948.0
007			cr nn 008mamaa
008			100301s2006 xxu| s |||| 0|eng d
020			_a9780387312408 _a99780387312408
024	7		_a10.1007/0-387-31240-4 _2doi
082	0	4	_a006.3 _223
100	1		_aNikolaev, Nikolay Y. _eauthor.
245	1	0	_aAdaptive Learning of Polynomial Networks _h[recurso electrónico] : _bGenetic Programming, Backpropagation and Bayesian Methods / _cby Nikolay Y. Nikolaev, Hitoshi Iba.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXIV, 316 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aGenetic and Evolutionary Computation
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.
650		0	_aCOMPUTER SCIENCE.
650		0	_aINFORMATION THEORY.
650		0	_aELECTRONIC DATA PROCESSING.
650		0	_aARTIFICIAL INTELLIGENCE.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aCOMPUTING METHODOLOGIES.
650	2	4	_aTHEORY OF COMPUTATION.
700	1		_aIba, Hitoshi. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387312392
830		0	_aGenetic and Evolutionary Computation
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-31240-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c57127 _d57127