One-Shot Learning with Bayesian Networks


Humans often make accurate inferences given a single exposure to a novel situation. Some of these inferences can be achieved by discovering and using near-deterministic relationships between attributes. Approaches based on Bayesian networks are good at discovering and using soft probabilistic relationships between attributes, but typically fail to identify and exploit near-deterministic relationships. Here we develop a Bayesian network approach that overcomes this limitation by learning a hyperparameter for each distribution in the network that specifies whether it is non-deterministic or near-deterministic. We apply our approach to one-shot learning problems based on a real-world database of immigration records, and show that it outperforms a more standard Bayesian network approach.

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