000			03147cam a2200409Ii 4500
001			95203
003			ES-MaUEC
005			20230102112652.0
006			m o d
007			cr cnu|||unuuu
008			170112s2017 si ob 000 0 eng d
020			_a9789811029158 _q(electronic bk.)
020			_a9811029156 _q(electronic bk.)
020			_z9789811029134 _q(print)
020			_z981102913X
035			_a(OCoLC)968211913 _z(OCoLC)969446267 _z(OCoLC)974651013 _z(OCoLC)981884354 _z(OCoLC)1005793175 _z(OCoLC)1011999229
040			_aN$T _cN$T _dGW5XE _dN$T _dIDEBK _dYDX _dOCLCF _dUAB _dNJR _dCNCGM _dUPM _dIOG _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dILO _dOTZ _dOCLCQ _dVT2 _dU3W _dES-MaUEC _bspa
050		4	_aQA278.5 _bK664 2017 EB
100	1		_aKong, Xiangyu, _eautor
245	1	0	_aPrincipal component analysis networks and algorithms _cXiangyu Kong, Changhua Hu, Zhansheng Duan.
264		1	_aSingapore _bSpringer _c[2017]
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aIntroduction -- Eigenvalue and singular value decomposition -- Principal component analysis neural networks -- Minor component analysis neural networks -- Dual purpose methods for principal and minor component analysis -- Deterministic discrete time system for PCA or MCA methods -- Generalized feature extraction method -- Coupled principal component analysis -- Singular feature extraction neural networks.
520	3		_aThis book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
650		7	_aAnálisis multivariante _2embne _0(OCoLC)fst01076520 _0 _9142349
700	1		_aDuan, Zhansheng, _eautor
700	1		_aHu, Changhua, _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-981-10-2915-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95203 _d95203 _x1