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On Valid and Invalid Methodologies for Experimentala Evaluations of EBL

Author
Segre, Alberto M.; Elkan, Charles P.; Russell, Alex
Abstract
A number of experimental evaluations of explanation-based learning (EBL) have appeared in the literature on machine learning. Closer examination of experimental methodologies used in the past reveals certain methodological flaws that call into question the conclusions drawn from these experiments. This paper illustrates some of the more common methodological problems, proposes a novel experimental framework for future empirical studies of EBL, and presents an example of an experiment performed within this new framework.
Date Issued
1990-05Publisher
Cornell University
Subject
computer science; technical report
Previously Published As
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR90-1126
Type
technical report