Communications of the ACM
A guide to expert systems
Machine learning of inductive bias
Machine learning of inductive bias
Information Processing Letters
Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Inferring decision trees using the minimum description length principle
Information and Computation
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Algorithmic Program DeBugging
Machine Learning
Machine Learning
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Building robust learning systems by combining induction and optimization
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Duce, an oracle-based approach to constructive induction
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
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This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, "universal" learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.