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: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin :
De Gruyter,
[2018]
|
| Schlagworte: | |
| Online-Zugang: | 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: | 1 online resource (338 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9783110518757 3110518759 3110518708 9783110518702 |
Internformat
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| 245 | 1 | 0 | |a Dynamic fuzzy machine learning / |c Li Fanzhang, Zhang Li, Zhang Zhao. |
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| 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. | ||
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| author | Li, Fanzhang Zhang, Li Zhang, Zhao |
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| contents | 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. |
| ctrlnum | (OCoLC)1020026051 |
| dewey-full | 006.31 |
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| id | ZDB-4-EBA-on1020026051 |
| illustrated | Not Illustrated |
| indexdate | 2024-10-25T16:24:08Z |
| institution | BVB |
| isbn | 9783110518757 3110518759 3110518708 9783110518702 |
| language | English |
| oclc_num | 1020026051 |
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| physical | 1 online resource (338 pages) |
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| spelling | Li, Fanzhang, author. https://id.oclc.org/worldcat/entity/E39PCjHDHWXPxQRMjXXMrQBv6q http://id.loc.gov/authorities/names/n2006065630 Dynamic fuzzy machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. Berlin : De Gruyter, [2018] ©2018 1 online resource (338 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. 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. 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Apprentissage automatique. Logique floue. COMPUTERS General. bisacsh Fuzzy logic fast Machine learning fast Zhang, Li, author. Zhang, Zhao, author. has work: Dynamic fuzzy machine learning (Text) https://id.oclc.org/worldcat/entity/E39PCH9q4yW97c64tBv7GXGg83 https://id.oclc.org/worldcat/ontology/hasWork Print version: Li, Fanzhang. Dynamic Fuzzy Machine Learning. Berlin/Boston : De Gruyter, ©2017 9783110518702 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1684621 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1684621 Volltext |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao Dynamic fuzzy machine learning / 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Apprentissage automatique. Logique floue. COMPUTERS General. bisacsh Fuzzy logic fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh93006704 |
| title | Dynamic fuzzy machine learning / |
| title_auth | Dynamic fuzzy machine learning / |
| title_exact_search | Dynamic fuzzy machine learning / |
| title_full | Dynamic fuzzy machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_fullStr | Dynamic fuzzy machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_full_unstemmed | Dynamic fuzzy machine learning / Li Fanzhang, Zhang Li, Zhang Zhao. |
| title_short | Dynamic fuzzy machine learning / |
| title_sort | dynamic fuzzy machine learning |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Fuzzy logic. http://id.loc.gov/authorities/subjects/sh93006704 Apprentissage automatique. Logique floue. COMPUTERS General. bisacsh Fuzzy logic fast Machine learning fast |
| topic_facet | Machine learning. Fuzzy logic. Apprentissage automatique. Logique floue. COMPUTERS General. Fuzzy logic Machine learning |
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| work_keys_str_mv | AT lifanzhang dynamicfuzzymachinelearning AT zhangli dynamicfuzzymachinelearning AT zhangzhao dynamicfuzzymachinelearning |