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...
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| Main Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Berlin ; Boston :
Walter de Gruyter, GmbH,
2018.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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 artificial 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, artificial 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. |
| Physical Description: | 1 online resource (xvi, 517 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9783110499506 3110499509 9783110498073 3110498073 |
Staff View
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| 245 | 1 | 0 | |a Lie group machine learning / |c Li Fanzhang, Zhang Li, Zhang Zhao. |
| 264 | 1 | |a Berlin ; |a Boston : |b Walter de Gruyter, GmbH, |c 2018. | |
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from digital title page (De Gruyter, viewed July 17, 2020). | |
| 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 artificial 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, artificial 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. | ||
| 505 | 0 | |a Lie group machine learning model -- Lie group subspace orbit generation learning -- Symplectic group learning -- Quantum group learning -- Lie group fibre bundle learning -- Lie group covering learning -- Lie group deep structure learning -- Lie group semi-supervised learning -- Lie group kernel learning -- Tensor learning -- Frame bundle connection learning -- Spectral estimation learning -- Finsler geometric learning -- Homology boundary learning -- Category representation learning -- Neuromorphic synergy learning. | |
| 545 | |a Li Zhang (M'08) received the degree of B.Sc. in1997 and the degree of Ph.D. in 2002 in electronic engineering from Xidian University, Xi'an, China. She is now a professor at the School of Computer Science and Technology, Soochow University, Suzhou, China. | ||
| 546 | |a In English. | ||
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| 650 | 0 | |a Lie groups. |0 http://id.loc.gov/authorities/subjects/sh85076786 | |
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| 700 | 1 | |a Zhang, Li, |e author. |u School of Computer Science and Technology, Soochow University, China | |
| 700 | 1 | |a Zhang, Zhao |c (Computer scientist), |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjtK9VC9dMx69XcPmgK7h3 |0 http://id.loc.gov/authorities/names/nb2015000018 | |
| 758 | |i has work: |a Lie group machine learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCG4hXcmGry3dMtJygmv6Td |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Li, Fanzhang. |t Lie group machine learning. |d Berlin ; Boston : Walter de Gruyter, GmbH, [2019] |z 9783110500684 |w (DLC) 2018951019 |w (OCoLC)1044854219 |
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Record in the Search Index
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| author | Li, Fanzhang Zhang, Li Zhang, Zhao (Computer scientist) |
| author_GND | http://id.loc.gov/authorities/names/n2006065630 http://id.loc.gov/authorities/names/nb2015000018 |
| author_facet | Li, Fanzhang Zhang, Li Zhang, Zhao (Computer scientist) |
| author_role | aut aut aut |
| author_sort | Li, Fanzhang |
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| contents | Lie group machine learning model -- Lie group subspace orbit generation learning -- Symplectic group learning -- Quantum group learning -- Lie group fibre bundle learning -- Lie group covering learning -- Lie group deep structure learning -- Lie group semi-supervised learning -- Lie group kernel learning -- Tensor learning -- Frame bundle connection learning -- Spectral estimation learning -- Finsler geometric learning -- Homology boundary learning -- Category representation learning -- Neuromorphic synergy learning. |
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| dewey-ones | 006 - Special computer methods |
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| dewey-search | 006.3/1 |
| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| genre | Electronic books. Electronic book. |
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| id | ZDB-4-EBA-on1066182573 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:46:38Z |
| institution | BVB |
| isbn | 9783110499506 3110499509 9783110498073 3110498073 |
| language | English |
| oclc_num | 1066182573 |
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| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 517 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Walter de Gruyter, GmbH, |
| record_format | marc |
| spelling | Li, Fanzhang, author. https://id.oclc.org/worldcat/entity/E39PCjHDHWXPxQRMjXXMrQBv6q http://id.loc.gov/authorities/names/n2006065630 Lie group machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. Berlin ; Boston : Walter de Gruyter, GmbH, 2018. ©2019 1 online resource (xvi, 517 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Online resource; title from digital title page (De Gruyter, viewed July 17, 2020). 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 artificial 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, artificial 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. Lie group machine learning model -- Lie group subspace orbit generation learning -- Symplectic group learning -- Quantum group learning -- Lie group fibre bundle learning -- Lie group covering learning -- Lie group deep structure learning -- Lie group semi-supervised learning -- Lie group kernel learning -- Tensor learning -- Frame bundle connection learning -- Spectral estimation learning -- Finsler geometric learning -- Homology boundary learning -- Category representation learning -- Neuromorphic synergy learning. Li Zhang (M'08) received the degree of B.Sc. in1997 and the degree of Ph.D. in 2002 in electronic engineering from Xidian University, Xi'an, China. She is now a professor at the School of Computer Science and Technology, Soochow University, Suzhou, China. In English. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Lie groups. http://id.loc.gov/authorities/subjects/sh85076786 Apprentissage automatique. Groupes de Lie. COMPUTERS General. bisacsh Lie groups fast Machine learning fast Electronic books. Electronic book. Zhang, Li, author. School of Computer Science and Technology, Soochow University, China Zhang, Zhao (Computer scientist), author. https://id.oclc.org/worldcat/entity/E39PCjtK9VC9dMx69XcPmgK7h3 http://id.loc.gov/authorities/names/nb2015000018 has work: Lie group machine learning (Text) https://id.oclc.org/worldcat/entity/E39PCG4hXcmGry3dMtJygmv6Td https://id.oclc.org/worldcat/ontology/hasWork Print version: Li, Fanzhang. Lie group machine learning. Berlin ; Boston : Walter de Gruyter, GmbH, [2019] 9783110500684 (DLC) 2018951019 (OCoLC)1044854219 |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao (Computer scientist) Lie group machine learning / Lie group machine learning model -- Lie group subspace orbit generation learning -- Symplectic group learning -- Quantum group learning -- Lie group fibre bundle learning -- Lie group covering learning -- Lie group deep structure learning -- Lie group semi-supervised learning -- Lie group kernel learning -- Tensor learning -- Frame bundle connection learning -- Spectral estimation learning -- Finsler geometric learning -- Homology boundary learning -- Category representation learning -- Neuromorphic synergy learning. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Lie groups. http://id.loc.gov/authorities/subjects/sh85076786 Apprentissage automatique. Groupes de Lie. COMPUTERS General. bisacsh Lie groups fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85076786 |
| title | Lie group machine learning / |
| title_auth | Lie group machine learning / |
| title_exact_search | Lie group machine learning / |
| title_full | Lie group machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_fullStr | Lie group machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_full_unstemmed | Lie group machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_short | Lie group machine learning / |
| title_sort | lie group machine learning |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Lie groups. http://id.loc.gov/authorities/subjects/sh85076786 Apprentissage automatique. Groupes de Lie. COMPUTERS General. bisacsh Lie groups fast Machine learning fast |
| topic_facet | Machine learning. Lie groups. Apprentissage automatique. Groupes de Lie. COMPUTERS General. Lie groups Machine learning Electronic books. Electronic book. |
| work_keys_str_mv | AT lifanzhang liegroupmachinelearning AT zhangli liegroupmachinelearning AT zhangzhao liegroupmachinelearning |