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Springer Explanation-Based Neural Network Learning Book

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Description

Explore the advancements in machine learning with Explanation-Based Neural Network Learning: A Lifelong Learning Approach. This technical text by Springer examines how lifelong learning addresses complex scenarios where a learner encounters a series of different learning tasks. By focusing on the synergy between these tasks, the book introduces the EBNN algorithm, which provides a method for transferring knowledge across multiple learning environments. Understanding how to exploit domain knowledge is essential for modern AI development. This book explains how EBNN uses accumulated knowledge from previous tasks to guide generalization in new ones. This approach allows the system to generalize more accurately while requiring less data than comparable methods. It is a valuable resource for those studying neural networks and efficient machine learning algorithms.

Key Features

Covers the principles of lifelong learning and how learners can find synergy among a series of different learning tasks.

Explains the EBNN machine learning algorithm designed to transfer knowledge across multiple different learning tasks.

Provides insight into using accumulated domain knowledge to guide the generalization process in new learning tasks.

Details how the EBNN method achieves more accurate generalization from less data compared to other methods.

Part of the Springer International Series in Engineering and Computer Science, volume 357.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 April 1996
Listed Since
15 December 2006

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No barcode data available

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