£129.99

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Springer Banach Space Valued Neural Network - AI Approximation Theory

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Description

Explore advanced neural network theory through the lens of Banach space mathematics with this comprehensive volume from Springer's Studies in Computational Intelligence series. This book examines both ordinary and fractional approximation methods for neural networks operating in Banach space settings, offering rigorous mathematical foundations for machine learning researchers and theoretical computer scientists. Whether you are developing cutting-edge AI models or studying functional analysis applications in neural computation, this text provides essential insights into how Banach space theory underpins modern approximation techniques and interpolation methods. Part of the prestigious Studies in Computational Intelligence collection (volume 1062), this work bridges pure mathematics and practical machine learning applications.

Key Features

Covers ordinary and fractional approximation approaches for Banach space valued neural networks.

Part of Springer's Studies in Computational Intelligence series, volume 1062.

Offers rigorous mathematical treatment suitable for advanced researchers in AI and machine learning.

Explores interpolation methods within Banach space frameworks for neural network theory.

Published by Springer, a leading academic publisher in computing and intelligence research.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
03 October 2023
Listed Since
06 September 2023

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