A practical Bayesian framework for backpropagation networks
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning words from sights and sounds: a computational model
Learning words from sights and sounds: a computational model
The evidence framework applied to classification networks
Neural Computation
Skill Acquisition Through Program-Level Imitation in a Real-Time Domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The association of perception and action is key to learning by observation in general, and to program-level task imitation in particular. The question is how to structure this information such that learning is tractable for resource-bounded agents. By introducing a combination of symbolic representation with Bayesian reasoning, we demonstrate both theoretical and empirical improvements to a general-purpose imitation system originally based on a model of infant social learning. We also show how prior task knowledge and selective attention can be rigorously incorporated via loss matrices and Automatic Relevance Determination respectively.