- Initialize $h$ to the most specific hypothesis in $H$
- For each positive training instance $x$
- For each attribute constraint ${a}_{i}$ in $h$:
- If the constraint ${a}_{i}$ in $h$ is satisfied by $x$ Then do nothing
- Else replace ${a}_{i}$ in $h$ by the next more general constraint that is satisfied by $x$

- For each attribute constraint ${a}_{i}$ in $h$:
- Output hypothesis $h$

- Guaranteed to output the most specific hypothesis within $H$ that is consistent with the positive training examples.
- Notice that negative examples are ignored.

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