Incremental Rule-based Learning
- Creators
- Higgins, Charles M.
- Goodman, Rodney M.
Abstract
In a system which learns to predict the value of an output variable given one or more input variables by looking at a set of examples, a rule-based knowledge representation provides not only a natural method of constructing a classifier, but also a human-readable explanation of what has been learned. Consider a rule of the form if y then x where y is a conjunction of values of input variables and x is a value of the output variable. The number of input variables in y is called the order of the rule. In previous work, a measure of the information content or "value" of such a rule has been developed (the J-measure. It has been shown in [3] that a classifier can be built from the rules obtained by a constrained search of all possible rules which performs comparably with other classifiers.
Additional Information
© 1991 IEEE. This work is supported in part by the Army Research Office under contract number DAAL03-89-K-0126 and in part by DARPA under contract number AFOSR-90-0199.Attached Files
Published - 00695344.pdf
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Additional details
- Eprint ID
- 93842
- Resolver ID
- CaltechAUTHORS:20190314-142001533
- Army Research Office (ARO)
- DAAL03-89-K-0126
- Defense Advanced Research Projects Agency (DARPA)
- Air Force Office of Scientific Research (AFOSR)
- AFOSR-90-0199
- Created
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2019-03-14Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field