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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin :
De Gruyter,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Li, Fanzhang, |e author. | |
| 245 | 1 | 0 | |a Dynamic fuzzy machine learning / |c Li Fanzhang, Zhang Li, Zhang Zhao. |
| 264 | 1 | |a Berlin : |b De Gruyter, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource (338 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro; Preface; Contents; 1. Dynamic fuzzy machine learning model; 2. Dynamic fuzzy autonomic learning subspace algorithm; 3. Dynamic fuzzy decision tree learning; 4. Concept learning based on dynamic fuzzy sets; 5. Semi-supervised multi-task learning based on dynamic fuzzy sets; 6. Dynamic fuzzy hierarchical relationships; 7. Multi-agent learning model based on dynamic fuzzy logic; 8. Appendix; Index. | |
| 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Fuzzy logic. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Logique floue. | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Fuzzy logic |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Zhang, Li, |e author. | |
| 700 | 1 | |a Zhang, Zhao, |e author. | |
| 758 | |i has work: |a Dynamic fuzzy machine learning (Text) |1 https://id.oclc.org/worldcat/entity/E39PCH9q4yW97c64tBv7GXGg83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Li, Fanzhang. |t Dynamic Fuzzy Machine Learning. |d Berlin/Boston : De Gruyter, ©2017 |z 9783110518702 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5157319 |z Texto completo |
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