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Machine Learning and Probabilistic Graphical Models for Decision Support Systems
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!
| Lieferstatus: |
 i.d.R. innert 7-14 Tagen versandfertig |
| Veröffentlichung: |
Oktober 2022
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| Genre: |
EDV / Informatik |
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anomaly detection / Bayesian Optimization Algorithm / bn / Centered Log Ratio / compositional data analysis / COMPUTERS / Machine Theory / cybersecurity anomaly detection / Data Set |
| ISBN: |
9781032039480 |
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EAN-Code:
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9781032039480 |
| Verlag: |
Taylor and Francis |
| Einband: |
Gebunden |
| Sprache: |
English
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| Dimensionen: |
H 254 mm / B 178 mm / D |
| Gewicht: |
1300 gr |
| Seiten: |
318 |
| Illustration: |
schwarz-weiss Illustrationen, farbige Illustrationen, Raster,schwarz-weiss, Raster, farbig, Zeichnungen, schwarz-weiss, Zeichnungen, farbig, Tabellen, schwarz-weiss, Tabellen, farbig |
| Bewertung: |
Titel bewerten / Meinung schreiben
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| Inhalt: |
| This book presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter. |
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