Unbiased Learner

We could define an unbiased hypothesis space by allowing arbitrary conjunctions, disjunctions, and negations.
The number of possible hypothesis (ignoring semantic repetition) is $2^{| X |}$ .
The Candidate-Elimination algorithm, if applied to this hypothesis space, will be unable to generalize beyond the observed examples!
So, in order to learn the concept we would need to present every single example.
If we use a partially-learned concept, then exactly half of the hypotheses will vote positive and half negative for every unseen example.