Explanation-Based Learning: A Comparison of Symbolic and Neural Network Approaches
Conference Paper, Proceedings of (ICML) International Conference on Machine Learning, pp. 197 - 204, June, 1993
Abstract
Explanation based learning has typically been considered a symbolic learning method. An explanation based learning method that utilizes purely neural network representations (called EBNN) has recently been developed, and has been shown to have several desirable properties, including robustness to errors in the domain theory. This paper briefly summarizes the EBNN algorithm, then explores the correspondence between this neural network based EBL method and EBL methods based on symbolic representations.
BibTeX
@conference{Mitchell-1993-15905,author = {Tom Mitchell and Sebastian Thrun},
title = {Explanation-Based Learning: A Comparison of Symbolic and Neural Network Approaches},
booktitle = {Proceedings of (ICML) International Conference on Machine Learning},
year = {1993},
month = {June},
editor = {P. Utgoff},
pages = {197 - 204},
publisher = {Morgan Kaufmann},
}
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