LEADER 03682nam 2200517 450 001 9910793294503321 005 20221213172941.0 010 $a3-11-049950-9 010 $a3-11-049807-3 024 7 $a10.1515/9783110499506 035 $a(CKB)4100000007123576 035 $a(DE-B1597)470633 035 $a(OCoLC)1066182573 035 $a(DE-B1597)9783110499506 035 $a(Au-PeEL)EBL5574847 035 $a(MiAaPQ)EBC5574847 035 $a(EXLCZ)994100000007123576 100 $a20200128d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLie group machine learning /$fLi Fanzhang, Zhang Li, Zhang Zhao 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2019] 210 4$dİ2019 215 $a1 online resource (534 pages) 300 $aIncludes index. 311 $a3-11-050068-X 327 $tFrontmatter --$tPreface --$tContents --$t1. Lie group machine learning model --$t2. Lie group subspace orbit generation learning --$t3. Symplectic group learning --$t4. Quantum group learning --$t5. Lie group fibre bundle learning --$t6. Lie group covering learning --$t7. Lie group deep structure learning --$t8. Lie group semi-supervised learning --$t9. Lie group kernel learning --$t10. Tensor learning --$t11. Frame bundle connection learning --$t12. Spectral estimation learning --$t13. Finsler geometric learning --$t14. Homology boundary learning --$t15. Category representation learning --$t16. Neuromorphic synergy learning --$t17. Appendix --$tAuthors --$tIndex 330 $aThis 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. 606 $aMachine learning 606 $aLie groups 615 0$aMachine learning. 615 0$aLie groups. 676 $a006.31 700 $aLi$b Fanzhang$01544040 702 $aZhang$b Li$f1967- 702 $aZhang$b Zhao 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910793294503321 996 $aLie group machine learning$93797916 997 $aUNINA