Representation Changes for Efficient Learning in Structural Domains.
Jean‐Daniel Zucker, Jean‐Gabriel Ganascia · 1996
This paper presents an efficient approach to address the task of learning from large number of learning examples in structural domains. While in attribute-value representations only one mapping is possible between descriptions, in first order logic representations there are potentially many mappings. Classic approaches consider all mappings and then define a restricted hypothesis space to cope with the intractability of exploring all mappings. Our approach is to select one particular type of mapping at a time and use it as a basis to define a new hypothesis space. We show that such a hypothesis space, called a Matching Space, may be represented using attribute-value pairs. In a Matching Space, it is therefore possible to use propositional learners. The concept descriptions found may then be mapped back into the initial first order logic representation. It appears that characterizing a Matching Space is equivalent to shifting the representation of examples: the new ...