Using explanation-based and empirical methods in theory revision
Using explanation-based and empirical methods in theory revision
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning to recognize promoter sequences in E. coli by modeling uncertainty in the training data
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
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Applied Intelligence
Maximizing Theory Accuracy Through Selective Reinterpretation
Machine Learning
Similarity-based retrieval for diverse bookshelf software repository users
CASCON '97 Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory.