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:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin ;Boston
De Gruyter
[2017]
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| Schlagwörter: | |
| Links: | https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 https://doi.org/10.1515/9783110520651 |
| 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) |
| Umfang: | 1 Online-Ressource (xiv, 324 Seiten) |
| ISBN: | 9783110520651 |
| DOI: | 10.1515/9783110520651 |
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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 |
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| discipline | Informatik |
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| language | English |
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| publisher | De Gruyter |
| record_format | marc |
| spelling | Li, Fanzhang (DE-588)1148670343 aut Dynamic Fuzzy Machine Learning Fanzhang Li, Li Zhang, Zhao Zhang Berlin ;Boston De Gruyter [2017] © 2018 1 Online-Ressource (xiv, 324 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Jan 2018) 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 In English Informationstechnik (DE-588)4026926-7 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Dynamisches Modell (DE-588)4150932-8 gnd rswk-swf Fuzzy-Menge (DE-588)4061868-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Informationstechnik (DE-588)4026926-7 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Fuzzy-Menge (DE-588)4061868-7 s Dynamisches Modell (DE-588)4150932-8 s 1\p DE-604 Zhang, Li (DE-588)1019056398 aut Zhang, Zhao (DE-588)1066750432 aut Erscheint auch als Druck-Ausgabe 978-3-11-051870-2 https://doi.org/10.1515/9783110520651 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| 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 |