Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Journal of Cognitive Neuroscience
Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Knowledge based Least Squares Twin support vector machines
Information Sciences: an International Journal
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Short Communication: The prediction of promoter sequences based on the chemical features
Expert Systems with Applications: An International Journal
Predicate Logic Based Image Grammars for Complex Pattern Recognition
International Journal of Computer Vision
Review: Hybrid expert systems: A survey of current approaches and applications
Expert Systems with Applications: An International Journal
Theory refinement on Bayesian networks
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Knowledge-Rich similarity-based classification
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Artificial Intelligence in Medicine
Paper: Polygenic trait analysis by neural network learning
Artificial Intelligence in Medicine
Extracting reducible knowledge from ANN with JBOS and FCANN approaches
Expert Systems with Applications: An International Journal
Central clustering of categorical data with automated feature weighting
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Standard algorithms for explanation-based learning require complete and correct knowledge bases. The KBANN system relaxes this constraint through the use of empirical learning methods to refine approximately correct knowledge. This knowledge is used to determine the structure of an artificial neural network and the weights on its links, thereby making the knowledge accessible for modification by neural learning. KBANN is evaluated by empirical tests in the domain of molecular biology. Networks created by KBANN are shown to be superior, in terms of their ability to correctly classify unseen examples, to randomly initialized neural networks, decision trees, "nearest neighbor" matching, and standard techniques reported in the biological literature. In addition, KBANN'S networks improve the initial knowledge in biologically interesting ways.