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Neural-based orthogonal data fitting = the EXIN neural networks /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural-based orthogonal data fitting/ Giansalvo Cirrincione, Maurizio Cirrincione.
其他題名:	the EXIN neural networks /
作者:	Cirrincione, Giansalvo,
其他作者:	Cirrincione, Maurizio,
出版者:	Hoboken, N.J. :Wiley, : c2010.,
面頁冊數:	xviii, 243 p., [12] p. of plates :ill. (some col.) ; : 25 cm.;
標題:	Neural networks (Computer science) -
電子資源:	An electronic book accessible through the World Wide Web; click for information
ISBN:	9780470638286 (electronic bk.)

Neural-based orthogonal data fitting = the EXIN neural networks /
Cirrincione, Giansalvo,1959-

Neural-based orthogonal data fittingthe EXIN neural networks /[electronic resource] :Giansalvo Cirrincione, Maurizio Cirrincione. - Hoboken, N.J. :Wiley,c2010. - xviii, 243 p., [12] p. of plates :ill. (some col.) ;25 cm. - Adaptive and learning systems for signal processing, communication, and control. - Adaptive and learning systems for signal processing, communications, and control..

Includes bibliographical references 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."--

Electronic reproduction.
Piscataway, N.J. :
IEEE,
2011.

Mode of access: World Wide Web.

ISBN: 9780470638286 (electronic bk.)

Source: 9780470638286IEEEhttp://ieeexplore.ieee.orgSubjects--Topical Terms:

528588
Neural networks (Computer science)
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: QA76.87 / .C525 2010

Dewey Class. No.: 006.3/2

Neural-based orthogonal data fitting = the EXIN neural networks /
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245 1 0 $a Neural-based orthogonal data fitting $h [electronic resource] : $b the EXIN neural networks / $c Giansalvo Cirrincione, Maurizio Cirrincione.
260 $a Hoboken, N.J. : $b Wiley, $c c2010.
300 $a xviii, 243 p., [12] p. of plates : $b ill. (some col.) ; $c 25 cm.
490 1 $a Adaptive and learning systems for signal processing, communication, and control
504 $a Includes bibliographical references and index.
520 $a "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."-- $c Provided by publisher.
533 $a Electronic reproduction. $b Piscataway, N.J. : $c IEEE, $d 2011. $n Mode of access: World Wide Web. $n System requirements: Web browser. $n Title from title screen (viewed on Apr. 26, 2011). $n Access may be restricted to users at subscribing institutions.
650 0 $a Neural networks (Computer science) $3 528588
650 0 $a Numerical analysis. $3 527939
650 0 $a Orthogonalization methods. $3 772802
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