Candidate-Elimination Summary

It will converge towards the hypothesis that correctly describes the target concept given that
- there are no errors in the training examples,
- there is some hypothesis H that correctly describes the target concept.
If there are errors, $S$ and $G$ will eventually converge to an empty version space.
For best performance, the next example should satisfy exactly half the hypotheses in the current version space (twenty questions).
Partially learned concepts can still be used (i.e., before converging to a single hypothesis)
- If all the hypotheses classify an instance as positive or negative then we can safely classify it as such.
- If there is a discrepancy then we could let the majority win. The vote could be viewed as a confidence measure.