Knowledge in context: a strategy for expert system maintenance
AI '88 Proceedings of the second Australian joint conference on Artificial intelligence
ML92 Proceedings of the ninth international workshop on Machine learning
Learning to control dynamic systems
Machine learning, neural and statistical classification
Generalising Ripple-Down Rules (Short Paper)
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
MonitoringKnowledge Acquisition Instead of Evaluating Knowledge Bases
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Simulations for Comparing Knowledge Acquisition and Machine Learning
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A simulation framework for knowledge acquisition evaluation
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Two decades of ripple down rules research
The Knowledge Engineering Review
An incremental knowledge acquisition-based system for critical domains
Expert Systems with Applications: An International Journal
An incremental knowledge acquisition-based system for supporting decisions in biomedical domains
Computer Methods and Programs in Biomedicine
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Evaluation of incremental knowledge acquisition with simulated experts
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a ...