Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning
Abstract
Abstract learning (EBL) component. In this paper we provide a brief An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.
Cite
Text
Pazzani and Brunk. "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning." AAAI Conference on Artificial Intelligence, 1993.Markdown
[Pazzani and Brunk. "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning." AAAI Conference on Artificial Intelligence, 1993.](https://mlanthology.org/aaai/1993/pazzani1993aaai-finding/)BibTeX
@inproceedings{pazzani1993aaai-finding,
title = {{Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning}},
author = {Pazzani, Michael J. and Brunk, Clifford},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1993},
pages = {328-334},
url = {https://mlanthology.org/aaai/1993/pazzani1993aaai-finding/}
}