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Springer Human Face Recognition Third-Order Neural Networks

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Description

Explore the advanced application of High-order synthetic neural network technology in this technical volume from Springer. This book examines how these networks facilitate transformation-invariant recognition of complex visual patterns. High-order networks offer a distinct advantage by requiring very little training data, which leads to significantly shorter training times. While these methods have already proven successful in performing transformation-invariant recognition of simple visual patterns with high recognition rates, this work moves toward addressing more practical problems. Specifically, the research transitions from binary patterns to the challenges of grayscale recognition. This text serves as a specialized resource for those studying the evolution of synthetic neural networks and their ability to handle complex, real-world visual data through advanced mathematical modeling.

Key Features

Examines the application of High-order synthetic neural network technology for recognition of complex visual patterns.

Provides insights into high recognition rates achieved through transformation-invariant recognition methods.

Discusses the efficiency of high-order networks that require minimal training data and short training times.

Explores the transition from simple binary patterns to more practical grayscale pattern recognition.

Part of The Springer International Series in Engineering and Computer Science (410).

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
30 June 1997
Listed Since
14 July 2013

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