Neural-based orthogonal data fitting: the EXIN neural networks
"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 al...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Hoboken, NJ
John Wiley & Sons
2010
|
Schriftenreihe: | Adaptive and learning systems for signal processing, communication, and control
|
Schlagworte: | |
Online-Zugang: | Inhaltsverzeichnis |
Zusammenfassung: | "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."-- |
Beschreibung: | Literaturverzeichnis S. 227 - 237 |
Beschreibung: | XVIII, 243 Seiten, [6] Blätter graph. Darst. |
ISBN: | 0471322709 9780471322702 |
Internformat
MARC
LEADER | 00000nam a2200000 c 4500 | ||
---|---|---|---|
001 | BV043438591 | ||
003 | DE-604 | ||
005 | 00000000000000.0 | ||
007 | t | ||
008 | 160308s2010 d||| |||| 00||| eng d | ||
010 | |a 2010033317 | ||
020 | |a 0471322709 |c hbk. : EUR 77,90 |9 0-471-32270-9 | ||
020 | |a 9780471322702 |c hbk. : EUR 77,90 |9 978-0-471-32270-2 | ||
035 | |a (OCoLC)729999794 | ||
035 | |a (DE-599)GBV610513214 | ||
040 | |a DE-604 |b ger |e rda | ||
041 | 0 | |a eng | |
049 | |a DE-83 | ||
050 | 0 | |a QA76.87 | |
082 | 0 | |a 006.3/2 | |
084 | |a 31.73 |2 bkl | ||
084 | |a 54.72 |2 bkl | ||
084 | |a 62-07 |2 msc | ||
100 | 1 | |a Cirrincione, Giansalvo |4 aut | |
245 | 1 | 0 | |a Neural-based orthogonal data fitting |b the EXIN neural networks |c Giansalvo Cirrincione ; Maurizio Cirrincione |
264 | 1 | |a Hoboken, NJ |b John Wiley & Sons |c 2010 | |
300 | |a XVIII, 243 Seiten, [6] Blätter |b graph. Darst. | ||
336 | |b txt |2 rdacontent | ||
337 | |b n |2 rdamedia | ||
338 | |b nc |2 rdacarrier | ||
490 | 0 | |a Adaptive and learning systems for signal processing, communication, and control | |
500 | |a Literaturverzeichnis S. 227 - 237 | ||
520 | 1 | |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."-- | |
650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Hauptkomponentenanalyse |0 (DE-588)4129174-8 |2 gnd |9 rswk-swf |
689 | 0 | 0 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
689 | 0 | 1 | |a Hauptkomponentenanalyse |0 (DE-588)4129174-8 |D s |
689 | 0 | |5 DE-604 | |
700 | 1 | |a Cirrincione, Maurizio |4 aut | |
856 | 4 | |m DE-601 |q pdf/application |u http://www.gbv.de/dms/ilmenau/toc/610513214.PDF |3 Inhaltsverzeichnis | |
999 | |a oai:aleph.bib-bvb.de:BVB01-028856141 |
Datensatz im Suchindex
_version_ | 1804176040792162304 |
---|---|
any_adam_object | |
author | Cirrincione, Giansalvo Cirrincione, Maurizio |
author_facet | Cirrincione, Giansalvo Cirrincione, Maurizio |
author_role | aut aut |
author_sort | Cirrincione, Giansalvo |
author_variant | g c gc m c mc |
building | Verbundindex |
bvnumber | BV043438591 |
callnumber-first | Q - Science |
callnumber-label | QA76 |
callnumber-raw | QA76.87 |
callnumber-search | QA76.87 |
callnumber-sort | QA 276.87 |
callnumber-subject | QA - Mathematics |
ctrlnum | (OCoLC)729999794 (DE-599)GBV610513214 |
dewey-full | 006.3/2 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 006 - Special computer methods |
dewey-raw | 006.3/2 |
dewey-search | 006.3/2 |
dewey-sort | 16.3 12 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik |
format | Book |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02481nam a2200457 c 4500</leader><controlfield tag="001">BV043438591</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">160308s2010 d||| |||| 00||| eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a">2010033317</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0471322709</subfield><subfield code="c">hbk. : EUR 77,90</subfield><subfield code="9">0-471-32270-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780471322702</subfield><subfield code="c">hbk. : EUR 77,90</subfield><subfield code="9">978-0-471-32270-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)729999794</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV610513214</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-83</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">QA76.87</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/2</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">62-07</subfield><subfield code="2">msc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cirrincione, Giansalvo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural-based orthogonal data fitting</subfield><subfield code="b">the EXIN neural networks</subfield><subfield code="c">Giansalvo Cirrincione ; Maurizio Cirrincione</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hoboken, NJ</subfield><subfield code="b">John Wiley & Sons</subfield><subfield code="c">2010</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVIII, 243 Seiten, [6] Blätter</subfield><subfield code="b">graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Adaptive and learning systems for signal processing, communication, and control</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Literaturverzeichnis S. 227 - 237</subfield></datafield><datafield tag="520" ind1="1" ind2=" "><subfield code="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."--</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Hauptkomponentenanalyse</subfield><subfield code="0">(DE-588)4129174-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Hauptkomponentenanalyse</subfield><subfield code="0">(DE-588)4129174-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cirrincione, Maurizio</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="m">DE-601</subfield><subfield code="q">pdf/application</subfield><subfield code="u">http://www.gbv.de/dms/ilmenau/toc/610513214.PDF</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-028856141</subfield></datafield></record></collection> |
id | DE-604.BV043438591 |
illustrated | Illustrated |
indexdate | 2024-07-10T07:25:52Z |
institution | BVB |
isbn | 0471322709 9780471322702 |
language | English |
lccn | 2010033317 |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028856141 |
oclc_num | 729999794 |
open_access_boolean | |
owner | DE-83 |
owner_facet | DE-83 |
physical | XVIII, 243 Seiten, [6] Blätter graph. Darst. |
publishDate | 2010 |
publishDateSearch | 2010 |
publishDateSort | 2010 |
publisher | John Wiley & Sons |
record_format | marc |
series2 | Adaptive and learning systems for signal processing, communication, and control |
spelling | Cirrincione, Giansalvo aut Neural-based orthogonal data fitting the EXIN neural networks Giansalvo Cirrincione ; Maurizio Cirrincione Hoboken, NJ John Wiley & Sons 2010 XVIII, 243 Seiten, [6] Blätter graph. Darst. txt rdacontent n rdamedia nc rdacarrier Adaptive and learning systems for signal processing, communication, and control Literaturverzeichnis S. 227 - 237 "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."-- Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Hauptkomponentenanalyse (DE-588)4129174-8 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Hauptkomponentenanalyse (DE-588)4129174-8 s DE-604 Cirrincione, Maurizio aut DE-601 pdf/application http://www.gbv.de/dms/ilmenau/toc/610513214.PDF Inhaltsverzeichnis |
spellingShingle | Cirrincione, Giansalvo Cirrincione, Maurizio Neural-based orthogonal data fitting the EXIN neural networks Neuronales Netz (DE-588)4226127-2 gnd Hauptkomponentenanalyse (DE-588)4129174-8 gnd |
subject_GND | (DE-588)4226127-2 (DE-588)4129174-8 |
title | Neural-based orthogonal data fitting the EXIN neural networks |
title_auth | Neural-based orthogonal data fitting the EXIN neural networks |
title_exact_search | Neural-based orthogonal data fitting the EXIN neural networks |
title_full | Neural-based orthogonal data fitting the EXIN neural networks Giansalvo Cirrincione ; Maurizio Cirrincione |
title_fullStr | Neural-based orthogonal data fitting the EXIN neural networks Giansalvo Cirrincione ; Maurizio Cirrincione |
title_full_unstemmed | Neural-based orthogonal data fitting the EXIN neural networks Giansalvo Cirrincione ; Maurizio Cirrincione |
title_short | Neural-based orthogonal data fitting |
title_sort | neural based orthogonal data fitting the exin neural networks |
title_sub | the EXIN neural networks |
topic | Neuronales Netz (DE-588)4226127-2 gnd Hauptkomponentenanalyse (DE-588)4129174-8 gnd |
topic_facet | Neuronales Netz Hauptkomponentenanalyse |
url | http://www.gbv.de/dms/ilmenau/toc/610513214.PDF |
work_keys_str_mv | AT cirrincionegiansalvo neuralbasedorthogonaldatafittingtheexinneuralnetworks AT cirrincionemaurizio neuralbasedorthogonaldatafittingtheexinneuralnetworks |