The Utility of Knowledge in Inductive Learning
Machine Learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Knowledge-Based Kernel Approximation
The Journal of Machine Learning Research
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Simpler knowledge-based support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
A simple and effective method for incorporating advice into kernel methods
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On the informativeness of the DNA promoter sequences domain theory
Journal of Artificial Intelligence Research
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Knowledge-based classification and regression methods are especially powerful forms of learning. They allow a system to take advantage of prior domain knowledge supplied either by a human user or another algorithm, combining that knowledge with data to produce accurate models. A limitation of the use of prior knowledge occurs when the provided knowledge is incorrect. Such knowledge likely still contains useful information, but knowledge-based learners might not be able to fully exploit such information. In fact, incorrect knowledge can lead to poorer models than result from knowledge-free learners. We present a support-vector method for incorporating and refining domain knowledge that not only allows the learner to make use of that knowledge, but also suggests changes to the provided knowledge. Our approach is built on the knowledge-based classification and regression methods presented by Fung, Mangasarian, & Shavlik (2002; 2003) and by Mangasarian, Shavlik, & Wild (2004). Experiments on artificial data sets with known properties, as well as on a real-world data set, demonstrate that our method learns more accurate models while also adjusting the provided rules in intuitive ways. Our new algorithm provides an appealing extension to knowledge-based, support-vector learning that is not only able to combine knowledge from rules with data, but is also able to use the data to modify and change those rules to better fit the data.