Futility of Bias-Free Learning

A learner that makes no a priori assumptions regarding the identity of the target concept has no rational basis for classifying any unseen instances.
So, what is the inductive bias?
Given an algorithm $L$ and a set of training instances $D_{c} = {⟨ x, c (x) ⟩}$ , let $L (x_{i}, D_{c})$ be the classification given to $x_{i}$ by $L$ after training on $D_{c}$ . The inductive bias of $L$ is any minimal set of assertions $B$ for any target concept $c$ and examples $D$ s.t. $\forall_{x_{i} \in X} B \land D_{c} \land x_{i} \to L (x_{i}, D_{c})$
For example, the inductive bias of the Candidate-Elimination algorithm (with voting) is the assumption that the target concept is contained in the hypothesis space.
That is, if we make that assumption then the classification follows logically (by deduction).