Buch, Englisch, 370 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 592 g
Joint International Conference, FAW-AAIM 2012, Beijing, China, May 14-16, 2012, Proceedings
Buch, Englisch, 370 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 592 g
Reihe: Theoretical Computer Science and General Issues
ISBN: 978-3-642-29699-4
Verlag: Springer
This book constitutes the refereed proceedings of the 6th International Frontiers of Algorithmics Workshop, FAW 2012, and the 8th International Conference on Algorithmic Aspects in Information and Management, AAIM 2012, jointly held in Beijing, China, in May 2012.
The 33 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on algorithms and data structures, algorithmic game theory and incentive analysis, biomedical imaging algorithms, communication networks and optimization, computational learning theory, knowledge discovery, and data mining, experimental algorithmic methodologies, optimization algorithms in economic and operations research, pattern recognition algorithms and trustworthy algorithms and trustworthy software.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik Mathematik Operations Research Spieltheorie
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen