The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics)

Rubinstein, Reuven Y.; Kroese, Dirk P.

ISBN 10: 038721240X / ISBN 13: 9780387212401

Edité par Springer, 2004

A propos de cet article

Hardcover without dust jacket. Bound in glossy pictorial paper over boards with red, black, and white lettering on the covers and spine. Binding is tight and secure. Corners are square. Previous owners name written in black ink on the FFEP. Pages are otherwise crisp, clean, and bright. Springer Science + Business Media. No date on title page. Copyright page dated 2004. 300 pages. A very good copy. We ship everyday from a real neighborhood bookstore. This description is written by an actual person, who is holding the book in front of them to make sure it?s properly described. Please contact us with questions or if you would like to see photographs. N° de réf. du vendeur F-13046

Détails bibliographiques

Titre : The Cross-Entropy Method: A Unified Approach...

Éditeur : Springer

Date d'édition : 2004

Reliure : hardcover

Etat : Very Good

A propos de ce titre

Synopsis :

The Cross-Entropy Method The cross-entropy (CE) method is one of the most significant developments in stochastic optimization and simulation in recent years. This book explains in detail how and why the CE method works. The CE method involves an iterative procedure where each iteration can be broken down into two phases: (a) generate a random data sample (trajectories, vectors, etc.) according to a specified mechanism; (b) update the parameters of the random mechanism based on this data in order to produce a better sample in the next iteration. The simplicity and versatility of the method is illustrated via a diverse collection of optimization and estimation problems.The book is aim...

Présentation de l'éditeur:

Rubinstein is the pioneer of the well-known score function and cross-entropy methods.

Accessible to 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.