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Metaheuristic Procedures for Training Neural Networks by Enrique Alba at Abbey's Bookshop,
Metaheuristic Procedures for Training Neural Networks
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Enrique Alba Rafael Martí

Metaheuristic Procedures for Training Neural Networks

Enrique Alba,  Rafael Martí

$260.95   $208.95

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English
Springer-Verlag New York Inc.
17 May 2006
Neural networks & fuzzy systems
Series: Operations Research/Computer Science Interfaces Series
Metaheuristic Procedures For Training Neural Networks provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, which reviews classical training methods, the chapters are divided into three main categories. The first one is devoted to local search based methods, including Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. The third part covers other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. Overall, the book's objective is engineered to provide a broad coverage of the concepts, methods, and tools of this important area of ANNs within the realm of continuous optimization.
Edited by:   ,
Imprint:   Springer-Verlag New York Inc.
Country of Publication:   United States
Edition:   2006 ed.
Volume:   35
Dimensions:   Height: 235mm,  Width: 155mm,  Spine: 15mm
Weight:   1.220kg
ISBN:   9780387334158
ISBN 10:   0387334157
Series:   Operations Research/Computer Science Interfaces Series
Pages:   252
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Reviews for Metaheuristic Procedures for Training Neural Networks

From the reviews: The strength of the book is its clear motivation to bring a new breath from metaheuristics into training of neural networks and integrate both sub-disciplines for the purpose of better exploitation of artificial intelligence approaches. ! The most benefiting reader of this book will perhaps be those who research on modelling data with ANN faced with difficulty of robust mapping with classical training algorithms. (S. Gazioglu, Journal of the Operational Research Society, Vol. 58 (12), 2007)


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