Dynamic Fuzzy Machine Learning:
Machine 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 car...
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin ;Boston
De Gruyter
[2017]
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| Schlagworte: | |
| Online-Zugang: | FAB01 FAW01 FHA01 FHI01 FHM01 FHR01 FKE01 FLA01 FWS01 FWS02 UBY01 UPA01 FCO01 Volltext |
| Zusammenfassung: | Machine 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 |
| Beschreibung: | Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Jan 2018) |
| Beschreibung: | 1 Online-Ressource (xiv, 324 Seiten) |
| ISBN: | 9783110520651 |
| DOI: | 10.1515/9783110520651 |
Internformat
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| 520 | |a Machine 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 | ||
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Datensatz im Suchindex
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| author | Li, Fanzhang Zhang, Li Zhang, Zhao |
| author_GND | (DE-588)1148670343 (DE-588)1019056398 (DE-588)1066750432 |
| author_facet | Li, Fanzhang Zhang, Li Zhang, Zhao |
| author_role | aut aut aut |
| author_sort | Li, Fanzhang |
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| discipline | Informatik |
| doi_str_mv | 10.1515/9783110520651 |
| format | Electronic eBook |
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| id | DE-604.BV044744288 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T13:23:10Z |
| institution | BVB |
| isbn | 9783110520651 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030140074 |
| oclc_num | 1022101027 |
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| physical | 1 Online-Ressource (xiv, 324 Seiten) |
| psigel | ZDB-23-DGG ZDB-23-DEI ZDB-23-DGG FAB_PDA_DGG ZDB-23-DGG FAW_PDA_DGG ZDB-23-DGG FHA_PDA_DGG ZDB-23-DEI ZDB-23-DEI17 ZDB-23-DEI FHR_ZDB_23_DEI17 ZDB-23-DGG FKE_PDA_DGG ZDB-23-DGG FLA_PDA_DGG ZDB-23-DGG UPA_PDA_DGG ZDB-23-DGG FCO_PDA_DGG |
| publishDate | 2017 |
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| publisher | De Gruyter |
| record_format | marc |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao Dynamic Fuzzy Machine Learning Dynamisches Modell (DE-588)4150932-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Informationstechnik (DE-588)4026926-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4150932-8 (DE-588)4061868-7 (DE-588)4026926-7 (DE-588)4193754-5 (DE-588)4033447-8 |
| title | Dynamic Fuzzy Machine Learning |
| title_auth | Dynamic Fuzzy Machine Learning |
| title_exact_search | Dynamic Fuzzy Machine Learning |
| title_full | Dynamic Fuzzy Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_fullStr | Dynamic Fuzzy Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_full_unstemmed | Dynamic Fuzzy Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang |
| title_short | Dynamic Fuzzy Machine Learning |
| title_sort | dynamic fuzzy machine learning |
| topic | Dynamisches Modell (DE-588)4150932-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Informationstechnik (DE-588)4026926-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Dynamisches Modell Fuzzy-Menge Informationstechnik Maschinelles Lernen Künstliche Intelligenz |
| url | https://doi.org/10.1515/9783110520651 |
| work_keys_str_mv | AT lifanzhang dynamicfuzzymachinelearning AT zhangli dynamicfuzzymachinelearning AT zhangzhao dynamicfuzzymachinelearning |