Applying Preference Biases to Conjunctive and Disjunctive Version Spaces

The paper considers conjunctive and disjunctive version spa- ce learning as an incomplete search in complete hypotheses spaces. The incomplete search is guided by preference biases which are implemented by procedures based on the instance-based boundary sets representation of version spaces. The conditions for tractability of this representation are defined. As a result we propose to use instance-based boundary sets as a basis for the computationally feasible application of preference biases to version spaces.