Transfer learning:
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge ; New York, NY
Cambridge University Press
2020
|
| Schlagwörter: | |
| Links: | https://www.gbv.de/dms/tib-ub-hannover/1687376255.pdf |
| Abstract: | "This book is about the foundations, methods, techniques and applications of transfer learning. Transfer learning deals with how learning systems can quickly adapt themselves to new situations, new tasks and new environments. Transfer learning is a particularly important area of machine learning, which we can understand from several angles. First, the ability to learn from small data seems to be a particularly strong aspect of human intelligence. For example, we observe that babies learn from only a few examples and can quickly and effectively generalize from the few examples to concepts. This ability to learn from small data can be partly explained by the ability of humans to leverage and adapt the previous experience and pre-trained models to help solve future target problems. Adaptation is an innate ability of intelligent beings and artificially intelligent agents should certainly be endowed with transfer-learning ability"-- |
| Beschreibung: | Includes bibliographical references (pages 336-376) and index |
| Umfang: | xi, 379 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107016903 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048210164 | ||
| 003 | DE-604 | ||
| 005 | 20220621 | ||
| 007 | t| | ||
| 008 | 220510s2020 xxka||| |||| 00||| eng d | ||
| 020 | |a 9781107016903 |c hardback |9 978-1-107-01690-3 | ||
| 024 | 7 | |a 10.1017/9781139061773 |2 doi | |
| 035 | |a (OCoLC)1162835952 | ||
| 035 | |a (DE-599)KXP1687376255 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a xxk |c XA-GB | ||
| 049 | |a DE-83 | ||
| 050 | 0 | |a Q325.5 | |
| 082 | 0 | |a 006.3/1 | |
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a 54.72 |2 bkl | ||
| 100 | 1 | |a Yang, Qiang |d 1961- |e Verfasser |0 (DE-588)135614120 |4 aut | |
| 245 | 1 | 0 | |a Transfer learning |c Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University) |
| 264 | 1 | |a Cambridge ; New York, NY |b Cambridge University Press |c 2020 | |
| 300 | |a xi, 379 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references (pages 336-376) and index | ||
| 505 | 8 | |a Instance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing | |
| 520 | 3 | |a "This book is about the foundations, methods, techniques and applications of transfer learning. Transfer learning deals with how learning systems can quickly adapt themselves to new situations, new tasks and new environments. Transfer learning is a particularly important area of machine learning, which we can understand from several angles. First, the ability to learn from small data seems to be a particularly strong aspect of human intelligence. For example, we observe that babies learn from only a few examples and can quickly and effectively generalize from the few examples to concepts. This ability to learn from small data can be partly explained by the ability of humans to leverage and adapt the previous experience and pre-trained models to help solve future target problems. Adaptation is an innate ability of intelligent beings and artificially intelligent agents should certainly be endowed with transfer-learning ability"-- | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Machine learning | |
| 653 | 0 | |a Artificial intelligence | |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Zhang, Yu |d 1982- |e Verfasser |0 (DE-588)1205188177 |4 aut | |
| 700 | 1 | |a Dai, Wenyuan |d 1983- |e Verfasser |0 (DE-588)1205188533 |4 aut | |
| 700 | 1 | |a Pan, Sinno Jialin |d 1980- |e Verfasser |0 (DE-588)120518841X |4 aut | |
| 776 | 0 | |z 9781139061773 | |
| 856 | 4 | 2 | |m B:DE-89 |m V:DE-601 |q pdf/application |u https://www.gbv.de/dms/tib-ub-hannover/1687376255.pdf |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033591025 | |
Datensatz im Suchindex
| _version_ | 1818989442903310336 |
|---|---|
| any_adam_object | |
| author | Yang, Qiang 1961- Zhang, Yu 1982- Dai, Wenyuan 1983- Pan, Sinno Jialin 1980- |
| author_GND | (DE-588)135614120 (DE-588)1205188177 (DE-588)1205188533 (DE-588)120518841X |
| author_facet | Yang, Qiang 1961- Zhang, Yu 1982- Dai, Wenyuan 1983- Pan, Sinno Jialin 1980- |
| author_role | aut aut aut aut |
| author_sort | Yang, Qiang 1961- |
| author_variant | q y qy y z yz w d wd s j p sj sjp |
| building | Verbundindex |
| bvnumber | BV048210164 |
| callnumber-first | Q - Science |
| callnumber-label | Q325 |
| callnumber-raw | Q325.5 |
| callnumber-search | Q325.5 |
| callnumber-sort | Q 3325.5 |
| callnumber-subject | Q - General Science |
| classification_rvk | ST 300 |
| contents | Instance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing |
| ctrlnum | (OCoLC)1162835952 (DE-599)KXP1687376255 |
| dewey-full | 006.3/1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/1 |
| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| 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>03753nam a2200517 c 4500</leader><controlfield tag="001">BV048210164</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220621 </controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">220510s2020 xxka||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781107016903</subfield><subfield code="c">hardback</subfield><subfield code="9">978-1-107-01690-3</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781139061773</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1162835952</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1687376255</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="044" ind1=" " ind2=" "><subfield code="a">xxk</subfield><subfield code="c">XA-GB</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-83</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">Q325.5</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Qiang</subfield><subfield code="d">1961-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)135614120</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Transfer learning</subfield><subfield code="c">Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge ; New York, NY</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xi, 379 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">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="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (pages 336-376) and index</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Instance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"This book is about the foundations, methods, techniques and applications of transfer learning. Transfer learning deals with how learning systems can quickly adapt themselves to new situations, new tasks and new environments. Transfer learning is a particularly important area of machine learning, which we can understand from several angles. First, the ability to learn from small data seems to be a particularly strong aspect of human intelligence. For example, we observe that babies learn from only a few examples and can quickly and effectively generalize from the few examples to concepts. This ability to learn from small data can be partly explained by the ability of humans to leverage and adapt the previous experience and pre-trained models to help solve future target problems. Adaptation is an innate ability of intelligent beings and artificially intelligent agents should certainly be endowed with transfer-learning ability"--</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</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="653" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><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="700" ind1="1" ind2=" "><subfield code="a">Zhang, Yu</subfield><subfield code="d">1982-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1205188177</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dai, Wenyuan</subfield><subfield code="d">1983-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1205188533</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pan, Sinno Jialin</subfield><subfield code="d">1980-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)120518841X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2=" "><subfield code="z">9781139061773</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">B:DE-89</subfield><subfield code="m">V:DE-601</subfield><subfield code="q">pdf/application</subfield><subfield code="u">https://www.gbv.de/dms/tib-ub-hannover/1687376255.pdf</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033591025</subfield></datafield></record></collection> |
| id | DE-604.BV048210164 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T19:38:33Z |
| institution | BVB |
| isbn | 9781107016903 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033591025 |
| oclc_num | 1162835952 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | xi, 379 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Yang, Qiang 1961- Verfasser (DE-588)135614120 aut Transfer learning Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University) Cambridge ; New York, NY Cambridge University Press 2020 xi, 379 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references (pages 336-376) and index Instance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing "This book is about the foundations, methods, techniques and applications of transfer learning. Transfer learning deals with how learning systems can quickly adapt themselves to new situations, new tasks and new environments. Transfer learning is a particularly important area of machine learning, which we can understand from several angles. First, the ability to learn from small data seems to be a particularly strong aspect of human intelligence. For example, we observe that babies learn from only a few examples and can quickly and effectively generalize from the few examples to concepts. This ability to learn from small data can be partly explained by the ability of humans to leverage and adapt the previous experience and pre-trained models to help solve future target problems. Adaptation is an innate ability of intelligent beings and artificially intelligent agents should certainly be endowed with transfer-learning ability"-- Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Machine learning Artificial intelligence Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Zhang, Yu 1982- Verfasser (DE-588)1205188177 aut Dai, Wenyuan 1983- Verfasser (DE-588)1205188533 aut Pan, Sinno Jialin 1980- Verfasser (DE-588)120518841X aut 9781139061773 B:DE-89 V:DE-601 pdf/application https://www.gbv.de/dms/tib-ub-hannover/1687376255.pdf Inhaltsverzeichnis |
| spellingShingle | Yang, Qiang 1961- Zhang, Yu 1982- Dai, Wenyuan 1983- Pan, Sinno Jialin 1980- Transfer learning Instance-based transfer learning -- Feature-based transfer learning -- Model-based transfer learning -- Relation-based transfer learning -- Heterogeneous transfer learning -- Adversarial transfer learning -- Transfer learning in reinforcement learning -- Multi-task learning -- Transfer learning theory -- Transitive transfer learning -- AutoTL : learning to transfer automatically -- Few-shot learning -- Lifelong machine learning -- Privacy-preserving transfer learning -- Transfer learning in computer vision -- Transfer learning in natural language processing -- Transfer learning in dialogue systems -- Transfer learning in recommender systems -- Transfer learning in bioinformatics -- Transfer learning in activity recognition -- Transfer learning in urban computing Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4033447-8 |
| title | Transfer learning |
| title_auth | Transfer learning |
| title_exact_search | Transfer learning |
| title_full | Transfer learning Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University) |
| title_fullStr | Transfer learning Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University) |
| title_full_unstemmed | Transfer learning Qiang Yang (Hong Kong University of Science and Technology), Yu Zhang (Southern University of Science and Technology), Wenyuan Dai (4Paradigm Co., Ltd.), Sinno Jialin Pan (Nanyang Technological University) |
| title_short | Transfer learning |
| title_sort | transfer learning |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Maschinelles Lernen Künstliche Intelligenz |
| url | https://www.gbv.de/dms/tib-ub-hannover/1687376255.pdf |
| work_keys_str_mv | AT yangqiang transferlearning AT zhangyu transferlearning AT daiwenyuan transferlearning AT pansinnojialin transferlearning |