Learning With Inverted Resolution
- Use inverse entailment to construct hypotheses that,
together with the background information, entail the training
data.
- Use sequential covering algorithm to iteratively learn a
set of Horn clauses in this way.
- Select a training example that is not yet covered by learned clauses.
- Use inverse resolution rule to generate candidate
hypothesis h that satisfies B ∧ h ∧ x → f(x),
where B = background knowledge plus any learned clauses.
- This is example-driven search.
- If multiple candidate hypotheses then choose one with
highest accuracy over the other examples.
José M. Vidal
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