Buch, Englisch, 185 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 312 g
ISBN: 978-3-642-63682-0
Verlag: Springer
This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung
- Mathematik | Informatik Mathematik Stochastik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
Weitere Infos & Material
I. Causal Models.- 1. Statistics, Causality, and Graphs.- 2. Causal Conjecture.- 3. Who Needs Counterfactuals?.- 4. Causality: Independence and Determinism.- II. Intelligent Data Management.- 5. Intelligent Data Analysis and Deep Understanding.- 6. Learning Algorithms in High Dimensional Spaces.- 7. Learning Linear Causal Models by MML Sampling.- 8. Game Theory Approach to Multicommodity Flow Network Vulnerability Analysis.- 9. On the Accuracy of Stochastic Complexity Approximations.- 10. AI Modelling for Data Quality Control Xiaohui Liu.- 11. New Directions in Text Categorization.