£94.37

Springer Neural Networks: Computational Models and Applications

9783540692256

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Description

Expand your knowledge of artificial intelligence with Neural Networks: Computational Models and Applications. Published by Springer as part of the Studies in Computational Intelligence series, this book provides a deep look into both theoretical and practical aspects of neural network technology. Readers will explore essential learning algorithms for feed-forward neural networks and examine the dynamical properties of recurrent neural networks. The text covers a wide range of computational intelligence topics, including winner-take-all networks and their uses in pattern recognition, uniform approximation, and image segmentation. This volume is designed to give you a quick but insightful understanding of complex computational models. It addresses practical challenges such as constrained optimization and NP-hard problems, making it a valuable resource for those studying computer science, AI, and machine learning.

Key Features

Covers essential learning algorithms for feed-forward neural networks to build a strong theoretical foundation.

Explores the various dynamical properties found in recurrent neural networks for advanced study.

Provides insights into winner-take-all networks and their role in broad computational intelligence manifolds.

Addresses practical applications in pattern recognition, image segmentation, and uniform approximation.

Discusses complex problem-solving including constrained optimization and NP-hard problems.

Delivers a quick and insightful understanding of diverse computational models for students and professionals.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 March 2007
Listed Since
24 January 2007

Barcode

No barcode data available

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