Neural based orthogonal data fitting : the EXIN neural networks / Giansalvo Cirrincione, Maurizio Cirrincione

By:

Cirrincione, Giansalvo

Contributor(s):

Cirrincione, Maurizio

Material type: Text

TextSeries: Adaptive and learning systems for signal processing, communication, and controlPublication details: Hoboken, NJ : John Wiley & Sons, c2010Description: xviii, 243 p., [12] p. of plates : ill. (some col.) ; 25 cmISBN:

9780471322702 (hardback)
0471322709 (hardback)

Subject(s):

Summary: Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Where most books on the subject are dedicated to PCA (principal component analysis) and consider MCA (minor component analysis) as simply a consequence, this is the fist book to start from the MCA problem and arrive at important conclusions about the PCA problem."-- Provided by publisher

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Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Open Shelf	UMPLIB PEKAN	QA76.87 .C57 2010 (Browse shelf(Opens below))	1	Available		0000059511

Includes bibliographical references (p. 227-237) and index

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Where most books on the subject are dedicated to PCA (principal component analysis) and consider MCA (minor component analysis) as simply a consequence, this is the fist book to start from the MCA problem and arrive at important conclusions about the PCA problem."-- Provided by publisher