A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is
AAAI Spring Symposia
A variety of research on theory and knowledge refinement that integrated knowledge engineering and machine learning was conducted in the 1990's. This work developed a variety of techniques for taking engineered knowledge in the form of propositional or first-order logical rule bases and revising them to fit empirical data using symbolic, probabilistic, and/or neural-network learning methods. We review this work to provide historical context for expanding these techniques to integrate modern knowledge engineering and machine learning methods.dblp:conf/aaaiss/MooneyS21 fatcat:kwjw4wfjd5bblmtbdvdo7367i4