£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

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No barcode data available

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