Lie Group Machine Learning:
This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advan...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin ; Boston
De Gruyter
[2018]
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| Schlagworte: | |
| Online-Zugang: | DE-1043 DE-1046 DE-Aug4 DE-M347 DE-92 DE-898 DE-859 DE-860 DE-706 DE-739 DE-858 Volltext |
| Zusammenfassung: | This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers |
| Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 23. Nov 2018) |
| Beschreibung: | 1 online resource (533 Seiten) |
| ISBN: | 9783110499506 |
| DOI: | 10.1515/9783110499506 |
Internformat
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| 520 | |a This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers | ||
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Datensatz im Suchindex
| _version_ | 1805069244155559936 |
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| adam_text | |
| any_adam_object | |
| author | Li, Fanzhang Zhang, Li Zhang, Zhao |
| author_GND | (DE-588)1148670343 |
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| author_role | aut aut aut |
| author_sort | Li, Fanzhang |
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| bvnumber | BV045879557 |
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| discipline | Informatik Mathematik |
| doi_str_mv | 10.1515/9783110499506 |
| format | Electronic eBook |
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| id | DE-604.BV045879557 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T04:02:57Z |
| institution | BVB |
| isbn | 9783110499506 |
| language | English |
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| spelling | Li, Fanzhang Verfasser (DE-588)1148670343 aut Lie Group Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang Berlin ; Boston De Gruyter [2018] © 2019 1 online resource (533 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on online resource; title from PDF title page (publisher's Web site, viewed 23. Nov 2018) This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers In English Lie-Gruppe (DE-588)4035695-4 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Lie-Gruppe (DE-588)4035695-4 s Kognitiver Prozess (DE-588)4140177-3 s Deep learning (DE-588)1135597375 s DE-604 Zhang, Li aut Zhang, Zhao aut Erscheint auch als Druck-Ausgabe 9783110500684 https://doi.org/10.1515/9783110499506 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao Lie Group Machine Learning Lie-Gruppe (DE-588)4035695-4 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4035695-4 (DE-588)4140177-3 (DE-588)1135597375 (DE-588)4193754-5 |
| title | Lie Group Machine Learning |
| title_auth | Lie Group Machine Learning |
| title_exact_search | Lie Group Machine Learning |
| title_full | Lie Group Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_fullStr | Lie Group Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_full_unstemmed | Lie Group Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_short | Lie Group Machine Learning |
| title_sort | lie group machine learning |
| topic | Lie-Gruppe (DE-588)4035695-4 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Lie-Gruppe Kognitiver Prozess Deep learning Maschinelles Lernen |
| url | https://doi.org/10.1515/9783110499506 |
| work_keys_str_mv | AT lifanzhang liegroupmachinelearning AT zhangli liegroupmachinelearning AT zhangzhao liegroupmachinelearning |