A comparison of concept identification in human learning and network learning with the generalized delta rule

Authors:
Michael Pazzani;Michael Dyer
Affiliations:
The Aerospace Corporation, Los Angeles, CA and UCLA AI Laboratory;UCLA AI Laboratory, Los Angeles, CA
Venue:
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Year:
1987

Citing 3
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Abstract

The generalized delta rule (which is also known as error back-propagation) is a significant advance over previous procedures for network learning. In this paper, we compare network learning using the generalized delta rule to human learning on two concept identification tasks: • Relative ease of concept identification • Generalizing from incomplete data