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: | DE-1043 DE-1046 DE-573 DE-M347 DE-898 DE-859 DE-860 DE-863 DE-862 DE-706 DE-739 DE-858 URL des Erstveröffentlichers |
| 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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| author | Li, Fanzhang Zhang, Li Zhang, Zhao |
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| discipline | Informatik |
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| id | DE-604.BV044744288 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-20T06:56:04Z |
| 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-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 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | De Gruyter |
| record_format | marc |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao Dynamic Fuzzy Machine Learning Informationstechnik (DE-588)4026926-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Dynamisches Modell (DE-588)4150932-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4026926-7 (DE-588)4033447-8 (DE-588)4150932-8 (DE-588)4061868-7 (DE-588)4193754-5 |
| 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 | Informationstechnik (DE-588)4026926-7 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Dynamisches Modell (DE-588)4150932-8 gnd Fuzzy-Menge (DE-588)4061868-7 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Informationstechnik Künstliche Intelligenz Dynamisches Modell Fuzzy-Menge Maschinelles Lernen |
| url | https://doi.org/10.1515/9783110520651 |
| work_keys_str_mv | AT lifanzhang dynamicfuzzymachinelearning AT zhangli dynamicfuzzymachinelearning AT zhangzhao dynamicfuzzymachinelearning |