Hyperrelations in version space Original Research Article

A version space is a set of all hypotheses consistent with a given set of training examples, delimited by the specific boundary and the general boundary. In existing studies [Machine Learning 17(1) (1994) 5; Proc. 5th IJCAI (1977) 305; Artificial Intelligence 18 (1982)] a hypothesis is a conjunction of attribute-value pairs, which is shown to have limited expressive power [Machine Learning, The McGraw-Hill Companies, Inc (1997)]. In a more expressive hypothesis space, e.g., disjunction of conjunction of attribute-value pairs, a general version space becomes uninteresting unless some restriction (inductive bias) is imposed [Machine Learning, The McGraw-Hill Companies, Inc (1997)].