Verfügbarkeit: Machine Learning in Industrial Applications: Insights Gained from Selected Studies

Machine Learning in Industrial Applications: Insights Gained from Selected Studies:

Gespeichert in:

Bibliographische Detailangaben
Beteilige Person:	Schmitz, Markus (VerfasserIn)
Format:	Hochschulschrift/Dissertation Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	Erlangen ; Nürnberg 2022
Schlagwörter:	Künstliche Intelligenz Überwachtes Lernen Deep Learning Industrie Machine learning Machine Learning Industrial Machine Learning Artificial Intelligence Machine Teaching Design Science Research Industrial Information Systems Hochschulschrift
Links:	https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-179485 https://d-nb.info/1250500753/34 https://open.fau.de/handle/openfau/17948
Umfang:	1 Online-Ressource (xix, 302 Seiten) Illustrationen, Diagramme

Internformat

MARC


LEADER	00000nam a2200000zc 4500
001	BV047813280
003	DE-604
005	20220223
007	cr\|uuu---uuuuu
008	220203s2022 gw a\|\|\| om\|\|\| 00\|\|\| eng d
015			\|a 22,O02 \|2 dnb
016	7		\|a 1250500753 \|2 DE-101
024	7		\|a urn:nbn:de:bvb:29-opus4-179485 \|2 urn
035			\|a (OCoLC)1296320756
035			\|a (DE-599)DNB1250500753
040			\|a DE-604 \|b ger \|e rda
041	0		\|8 3\p \|a eng
044			\|a gw \|c XA-DE-BY
049			\|a DE-384 \|a DE-473 \|a DE-703 \|a DE-1051 \|a DE-824 \|a DE-29 \|a DE-12 \|a DE-91 \|a DE-19 \|a DE-1049 \|a DE-92 \|a DE-739 \|a DE-898 \|a DE-355 \|a DE-706 \|a DE-20 \|a DE-1102 \|a DE-860 \|a DE-2174 \|a DE-862
084			\|8 1\p \|a 006.31 \|2 23ksdnb
084			\|8 2\p \|a 004 \|2 23sdnb
100	1		\|a Schmitz, Markus \|0 (DE-588)1250940125 \|4 aut
245	1	0	\|a Machine Learning in Industrial Applications: Insights Gained from Selected Studies \|c vorgelegt von Markus Schmitz
246	1	3	\|a Maschinelles Lernen in industriellen Anwendungen: Erkenntnisse aus ausgewählten Studien
264		1	\|a Erlangen ; Nürnberg \|c 2022
300			\|a 1 Online-Ressource (xix, 302 Seiten) \|b Illustrationen, Diagramme
336			\|b txt \|2 rdacontent
337			\|b c \|2 rdamedia
338			\|b cr \|2 rdacarrier
502			\|b Dissertation \|c Friedrich-Alexander-Universität Erlangen-Nürnberg \|d 2021
583	1		\|a Archivierung/Langzeitarchivierung gewährleistet \|5 DE-101 \|2 pdager
650	0	7	\|a Künstliche Intelligenz \|0 (DE-588)4033447-8 \|2 gnd \|9 rswk-swf
650	0	7	\|a Überwachtes Lernen \|0 (DE-588)4580264-6 \|2 gnd \|9 rswk-swf
650	0	7	\|a Deep Learning \|0 (DE-588)1135597375 \|2 gnd \|9 rswk-swf
650	0	7	\|a Industrie \|0 (DE-588)4026779-9 \|2 gnd \|9 rswk-swf
650	0	7	\|8 4\p \|a Machine learning \|0 (DLC)sh85079324 \|0 http://id.loc.gov/authorities/subjects/sh85079324 \|2 lcsh
653			\|a Machine Learning
653			\|a Deep Learning
653			\|a Industrial Machine Learning
653			\|a Artificial Intelligence
653			\|a Machine Teaching
653			\|a Design Science Research
653			\|a Industrial Information Systems
655		7	\|0 (DE-588)4113937-9 \|a Hochschulschrift \|2 gnd-content
689	0	0	\|a Deep Learning \|0 (DE-588)1135597375 \|D s
689	0	1	\|a Überwachtes Lernen \|0 (DE-588)4580264-6 \|D s
689	0	2	\|a Industrie \|0 (DE-588)4026779-9 \|D s
689	0	3	\|a Künstliche Intelligenz \|0 (DE-588)4033447-8 \|D s
689	0		\|5 DE-604
856	4	0	\|u https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-179485 \|x Resolving-System \|z kostenfrei \|3 Volltext
856	4	0	\|u https://d-nb.info/1250500753/34 \|x Langzeitarchivierung Nationalbibliothek \|z kostenfrei \|3 Volltext
856	4	0	\|q application/pdf \|u https://open.fau.de/handle/openfau/17948 \|x Verlag \|z kostenfrei \|3 Volltext
883	0		\|8 1\p \|a aepkn \|c 0,89367 \|d 20220129 \|q DE-101 \|u https://d-nb.info/provenance/plan#aepkn
883	0		\|8 2\p \|a aepsg \|c 0,96902 \|d 20220129 \|q DE-101 \|u https://d-nb.info/provenance/plan#aepsg
883	1		\|8 3\p \|a npi \|d 20220128 \|q DE-101 \|u https://d-nb.info/provenance/plan#npi
883	0		\|8 4\p \|a aeplcsh \|c 0,03571 \|d 20220129 \|q DE-101 \|u https://d-nb.info/provenance/plan#aeplcsh
912			\|a ebook
943	1		\|a oai:aleph.bib-bvb.de:BVB01-033196741

Datensatz im Suchindex

DE-BY-TUM_katkey	2609492
_version_	1823989086885511168
adam_text
any_adam_object
author	Schmitz, Markus
author_GND	(DE-588)1250940125
author_facet	Schmitz, Markus
author_role	aut
author_sort	Schmitz, Markus
author_variant	m s ms
building	Verbundindex
bvnumber	BV047813280
collection	ebook
ctrlnum	(OCoLC)1296320756 (DE-599)DNB1250500753
format	Thesis Electronic eBook
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV047813280</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220223</controlfield><controlfield tag="007">cr\|uuu---uuuuu</controlfield><controlfield tag="008">220203s2022 gw a\|\|\| om\|\|\| 00\|\|\| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">22,O02</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1250500753</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">urn:nbn:de:bvb:29-opus4-179485</subfield><subfield code="2">urn</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1296320756</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1250500753</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="8">3\p</subfield><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BY</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-1051</subfield><subfield code="a">DE-824</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-12</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-19</subfield><subfield code="a">DE-1049</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-898</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-706</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-1102</subfield><subfield code="a">DE-860</subfield><subfield code="a">DE-2174</subfield><subfield code="a">DE-862</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="8">1\p</subfield><subfield code="a">006.31</subfield><subfield code="2">23ksdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="8">2\p</subfield><subfield code="a">004</subfield><subfield code="2">23sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Schmitz, Markus</subfield><subfield code="0">(DE-588)1250940125</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning in Industrial Applications: Insights Gained from Selected Studies</subfield><subfield code="c">vorgelegt von Markus Schmitz</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">Maschinelles Lernen in industriellen Anwendungen: Erkenntnisse aus ausgewählten Studien</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Erlangen ; Nürnberg</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xix, 302 Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</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">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="b">Dissertation</subfield><subfield code="c">Friedrich-Alexander-Universität Erlangen-Nürnberg</subfield><subfield code="d">2021</subfield></datafield><datafield tag="583" ind1="1" ind2=" "><subfield code="a">Archivierung/Langzeitarchivierung gewährleistet</subfield><subfield code="5">DE-101</subfield><subfield code="2">pdager</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Überwachtes Lernen</subfield><subfield code="0">(DE-588)4580264-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep Learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Industrie</subfield><subfield code="0">(DE-588)4026779-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="8">4\p</subfield><subfield code="a">Machine learning</subfield><subfield code="0">(DLC)sh85079324</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield><subfield code="2">lcsh</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Machine Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Deep Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Industrial Machine Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Artificial Intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Machine Teaching</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Design Science Research</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Industrial Information Systems</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4113937-9</subfield><subfield code="a">Hochschulschrift</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Deep Learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Überwachtes Lernen</subfield><subfield code="0">(DE-588)4580264-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Industrie</subfield><subfield code="0">(DE-588)4026779-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-179485</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://d-nb.info/1250500753/34</subfield><subfield code="x">Langzeitarchivierung Nationalbibliothek</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="q">application/pdf</subfield><subfield code="u">https://open.fau.de/handle/openfau/17948</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="883" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">aepkn</subfield><subfield code="c">0,89367</subfield><subfield code="d">20220129</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#aepkn</subfield></datafield><datafield tag="883" ind1="0" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">aepsg</subfield><subfield code="c">0,96902</subfield><subfield code="d">20220129</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#aepsg</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">3\p</subfield><subfield code="a">npi</subfield><subfield code="d">20220128</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#npi</subfield></datafield><datafield tag="883" ind1="0" ind2=" "><subfield code="8">4\p</subfield><subfield code="a">aeplcsh</subfield><subfield code="c">0,03571</subfield><subfield code="d">20220129</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#aeplcsh</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ebook</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033196741</subfield></datafield></record></collection>
genre	(DE-588)4113937-9 Hochschulschrift gnd-content
genre_facet	Hochschulschrift
id	DE-604.BV047813280
illustrated	Illustrated
indexdate	2025-02-13T09:00:45Z
institution	BVB
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-033196741
oclc_num	1296320756
open_access_boolean	1
owner	DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-824 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-860 DE-2174 DE-862 DE-BY-FWS
owner_facet	DE-384 DE-473 DE-BY-UBG DE-703 DE-1051 DE-824 DE-29 DE-12 DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-1049 DE-92 DE-739 DE-898 DE-BY-UBR DE-355 DE-BY-UBR DE-706 DE-20 DE-1102 DE-860 DE-2174 DE-862 DE-BY-FWS
physical	1 Online-Ressource (xix, 302 Seiten) Illustrationen, Diagramme
psigel	ebook
publishDate	2022
publishDateSearch	2022
publishDateSort	2022
record_format	marc
spellingShingle	Schmitz, Markus Machine Learning in Industrial Applications: Insights Gained from Selected Studies Künstliche Intelligenz (DE-588)4033447-8 gnd Überwachtes Lernen (DE-588)4580264-6 gnd Deep Learning (DE-588)1135597375 gnd Industrie (DE-588)4026779-9 gnd 4\p Machine learning (DLC)sh85079324 http://id.loc.gov/authorities/subjects/sh85079324 lcsh
subject_GND	(DE-588)4033447-8 (DE-588)4580264-6 (DE-588)1135597375 (DE-588)4026779-9 (DLC)sh85079324 http://id.loc.gov/authorities/subjects/sh85079324 (DE-588)4113937-9
title	Machine Learning in Industrial Applications: Insights Gained from Selected Studies
title_alt	Maschinelles Lernen in industriellen Anwendungen: Erkenntnisse aus ausgewählten Studien
title_auth	Machine Learning in Industrial Applications: Insights Gained from Selected Studies
title_exact_search	Machine Learning in Industrial Applications: Insights Gained from Selected Studies
title_full	Machine Learning in Industrial Applications: Insights Gained from Selected Studies vorgelegt von Markus Schmitz
title_fullStr	Machine Learning in Industrial Applications: Insights Gained from Selected Studies vorgelegt von Markus Schmitz
title_full_unstemmed	Machine Learning in Industrial Applications: Insights Gained from Selected Studies vorgelegt von Markus Schmitz
title_short	Machine Learning in Industrial Applications: Insights Gained from Selected Studies
title_sort	machine learning in industrial applications insights gained from selected studies
topic	Künstliche Intelligenz (DE-588)4033447-8 gnd Überwachtes Lernen (DE-588)4580264-6 gnd Deep Learning (DE-588)1135597375 gnd Industrie (DE-588)4026779-9 gnd 4\p Machine learning (DLC)sh85079324 http://id.loc.gov/authorities/subjects/sh85079324 lcsh
topic_facet	Künstliche Intelligenz Überwachtes Lernen Deep Learning Industrie Machine learning Hochschulschrift
url	https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-179485 https://d-nb.info/1250500753/34 https://open.fau.de/handle/openfau/17948
work_keys_str_mv	AT schmitzmarkus machinelearninginindustrialapplicationsinsightsgainedfromselectedstudies AT schmitzmarkus maschinelleslerneninindustriellenanwendungenerkenntnisseausausgewahltenstudien

Verfügbarkeit

Bestellen (Login erforderlich)

Online lesen (frei zugänglich)
Paper/Kapitel scannen lassen