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008 210830t20172018gw fo d z eng d
020 _a9783110520651
024 7 _a10.1515/9783110520651
_2doi
035 _a(DE-B1597)473550
035 _a(OCoLC)1024050311
040 _aDE-B1597
_beng
_cDE-B1597
_erda
041 0 _aeng
044 _agw
_cDE
050 4 _aQ325.5
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.31
_223
084 _aST 330
_2rvk
_0(DE-625)rvk/143663:
100 1 _aLi, Fanzhang,
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976492
245 1 0 _aDynamic Fuzzy Machine Learning /
_cFanzhang Li, Li Zhang, Zhao Zhang.
264 1 _aBerlin ;
_aBoston :
_bDe Gruyter,
_c[2017]
264 4 _c©2018
300 _a1 online resource (XIV, 323 p.)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 0 _tFrontmatter --
_tPreface --
_tContents --
_t1. Dynamic fuzzy machine learning model --
_t2. Dynamic fuzzy autonomic learning subspace algorithm --
_t3. Dynamic fuzzy decision tree learning --
_t4. Concept learning based on dynamic fuzzy sets --
_t5. Semi-supervised multi-task learning based on dynamic fuzzy sets --
_t6. Dynamic fuzzy hierarchical relationships --
_t7. Multi-agent learning model based on dynamic fuzzy logic --
_t8. Appendix --
_tIndex
506 0 _arestricted access
_uhttp://purl.org/coar/access_right/c_16ec
_fonline access with authorization
_2star
520 _aMachine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. 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, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.
538 _aMode of access: Internet via World Wide Web.
546 _aIn English.
588 0 _aDescription based on online resource; title from PDF title page (publisher's Web site, viewed 30. Aug 2021)
650 0 _aFuzzy logic.
_93518
650 0 _aMachine learning.
_91831
650 7 _aCOMPUTERS / Intelligence (AI) & Semantics.
_2bisacsh
_976493
700 1 _aFanzhang, Li,
_econtributor.
_4ctb
_4https://id.loc.gov/vocabulary/relators/ctb
_976494
700 1 _aZhang, Li,
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976495
700 1 _aZhang, Zhao,
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976496
773 0 8 _iTitle is part of eBook package:
_dDe Gruyter
_tDG Plus eBook-Package 2018
_z9783110719550
773 0 8 _iTitle is part of eBook package:
_dDe Gruyter
_tEBOOK PACKAGE COMPLETE 2017
_z9783110540550
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773 0 8 _iTitle is part of eBook package:
_dDe Gruyter
_tEBOOK PACKAGE COMPLETE ENGLISH 2017
_z9783110625264
773 0 8 _iTitle is part of eBook package:
_dDe Gruyter
_tEBOOK PACKAGE Engineering, Computer Sciences 2017
_z9783110547757
_oZDB-23-DEI
776 0 _cEPUB
_z9783110518757
776 0 _cprint
_z9783110518702
856 4 0 _uhttps://doi.org/10.1515/9783110520651
856 4 0 _uhttps://www.degruyter.com/isbn/9783110520651
856 4 2 _3Cover
_uhttps://www.degruyter.com/cover/covers/9783110520651.jpg
912 _a978-3-11-062526-4 EBOOK PACKAGE COMPLETE ENGLISH 2017
_b2017
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