Buch, Englisch, 301 Seiten, Book w. online files / update, Format (B × H): 164 mm x 244 mm, Gewicht: 584 g
A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning
Buch, Englisch, 301 Seiten, Book w. online files / update, Format (B × H): 164 mm x 244 mm, Gewicht: 584 g
Reihe: Information Science and Statistics
ISBN: 978-0-387-21240-1
Verlag: Springer
The cross-entropy (CE) method is one of the most significant developments in randomized optimization and simulation in recent years. This book explains in detail how and why the CE method works. The simplicity and versatility of the method is illustrated via a diverse collection of optimization and estimation problems. The book is aimed at a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Technische Wissenschaften Technik Allgemein Modellierung & Simulation
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
Weitere Infos & Material
1 Preliminaries.- 2 A Tutorial Introduction to the Cross-Entropy Method.- 3 Efficient Simulation via Cross-Entropy.- 4 Combinatorial Optimization via Cross-Entropy.- 5 Continuous Optimization and Modifications.- 6 Noisy Optimization with CE.- 7 Applications of CE to COPs.- 8 Applications of CE to Machine Learning.- A Example Programs.- A.1 Rare Event Simulation.- A.2 The Max-Cut Problem.- A.3 Continuous Optimization via the Normal Distribution.- A.4 FACE.- A.5 Rosenbrock.- A.6 Beta Updating.- A.7 Banana Data.- References.
