Learning meta knowledge for database checking

Authors:
Jeffrey C. Schlimmer
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Year:
1991

Citing 6
Cited 0

Expert systems for configuration at Digital: XCON and beyond

Communications of the ACM
Learning classification rules using Bayes

Proceedings of the sixth international workshop on Machine learning
Learning and relearning in Boltzmann machines

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
On the Handling of Continuous-Valued Attributes in Decision Tree Generation

Machine Learning
Adaptive information retrieval: machine learning in associative networks (connectionist, free-text, browsing, feedback)

Adaptive information retrieval: machine learning in associative networks (connectionist, free-text, browsing, feedback)
Generating production rules from decision trees

IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Building a large-scale system often involves creating a large knowledge store, and as these grow and are maintained by a number of individuals, errors are inevitable. Exploring databases as a specialization of knowledge stores, this paper studies the hypothesis that descriptive, learned models can be prescriptively used to find errors. To that end, it describes an implemented system called CARPER. Applying CARPER to a real-world database demonstrates the viability of the approach and establishes a baseline of performance for future research.