The Utility of Knowledge in Inductive Learning
Machine Learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Knowledge-based artificial neural networks
Artificial Intelligence
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
Building probabilistic models for natural language
Building probabilistic models for natural language
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Improving Metrical Grammar with Grammar Expansion
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Variational bayesian grammar induction for natural language
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be "shared" across several output classes or even may not contribute ...