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Lie Group Machine Learning.

By: Li, Fanzhang.
Contributor(s): Zhang, Li | Zhang, Zhao.
Material type: materialTypeLabelBookPublisher: Berlin/Boston : De Gruyter, Inc., 2018Copyright date: �2019Description: 1 online resource (534 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783110499506.Subject(s): Machine learningGenre/Form: Electronic books.DDC classification: 006.3/1 Online resources: Click to View
Contents:
Intro -- Preface -- Contents -- 1. Lie group machine learning model -- 2. Lie group subspace orbit generation learning -- 3. Symplectic group learning -- 4. Quantum group learning -- 5. Lie group fibre bundle learning -- 6. Lie group covering learning -- 7. Lie group deep structure learning -- 8. Lie group semi-supervised learning -- 9. Lie group kernel learning -- 10. Tensor learning -- 11. Frame bundle connection learning -- 12. Spectral estimation learning -- 13. Finsler geometric learning -- 14. Homology boundary learning -- 15. Category representation learning -- 16. Neuromorphic synergy learning -- 17. Appendix -- Authors -- Index.
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Item type Current location Collection Call number URL Copy number Status Date due Item holds
E-book E-book IUKL Library
Subscripti https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=5574847 1 Available
Total holds: 0

Intro -- Preface -- Contents -- 1. Lie group machine learning model -- 2. Lie group subspace orbit generation learning -- 3. Symplectic group learning -- 4. Quantum group learning -- 5. Lie group fibre bundle learning -- 6. Lie group covering learning -- 7. Lie group deep structure learning -- 8. Lie group semi-supervised learning -- 9. Lie group kernel learning -- 10. Tensor learning -- 11. Frame bundle connection learning -- 12. Spectral estimation learning -- 13. Finsler geometric learning -- 14. Homology boundary learning -- 15. Category representation learning -- 16. Neuromorphic synergy learning -- 17. Appendix -- Authors -- Index.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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